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Identifying and locating diseases in chest X-rays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar structures in the left and right parts of the chest, such as ribs, lung fields and bronchial tubes. This kind of similarities can be used to identify diseases in chest X-rays, according to the experience of broad-certificated radiologists. Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals. First of all, under the guidance of the spine line, the spatial transformer network is employed to extract local contralateral patches, which can provide valuable context information for disease proposals. Then, we build up a specific module, based on both additive and subtractive operations, to fuse the features of the disease proposal and the contralateral patch. Our method can be integrated into both fully and weakly supervised disease detection frameworks. It achieves 33.17 AP50 on a carefully annotated private chest X-ray dataset which contains 31,000 images. Experiments on the NIH chest X-ray dataset indicate that our method achieves state-of-the-art performance in weakly-supervised disease localization.

Remote sensing image scene classification (RSISC) has garnered significant attention in recent years. Numerous methods have been put forward in an attempt to tackle this issue, particularly leveraging deep learning methods that have shown promising performance in classifying remote sensing image (RSI). However, it is widely recognized that deep learning methods typically require a substantial amount of labeled data to effectively converge. Acquiring a sufficient quantity of labeled data often necessitates significant human and material resources. Hence, few-shot RSISC has become highly meaningful. Fortunately, the recently proposed deep nearest neighbor neural network based on the attention mechanism (DN4AM) model incorporates episodic training and class-related attention mechanisms, effectively reducing the impact of background noise regions on classification results. Nevertheless, the DN4AM model does not address the problem of significant intra-class variability and substantial inter-class similarities observed in RSI scenes. Therefore, the discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) is proposed to address the few-shot RSISC task. Our method makes three contributions. Firstly, we introduce center loss to enhance the intra-class feature compactness. Secondly, we utilize the deep local-global descriptor (DLGD) to increase inter-class feature differentiation. Lastly, we modify the Softmax loss by incorporating cosine margin to amplify the inter-class feature dissimilarity. Experiments are conducted on three diverse RSI datasets to gauge the efficacy of our approach. Through comparative analysis with various cutting-edge methods including MatchingNet, RelationNet, MAML, Meta-SGD, DN4, and DN4AM, our approach showcases promising outcomes in the few-shot RSISC task. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCNs inevitably encounter the limitations of non-robustness and low classification accuracy when labeled nodes are scarce. To address the two issues, the deep graph convolutional generative adversarial network (DGCGAN), a model combining GCN and deep convolutional generative adversarial networks (DCGAN), is proposed in this paper. First, the graph data is mapped to a highly nonlinear space by using the topology and attribute information of the graph for symmetric normalized Laplacian transform. Then, through the feature-structured enhanced module, the node features are expanded into regular structured data, such as images and sequences, which are input to DGCGAN as positive samples, thus expanding the sample capacity. In addition, the feature-enhanced (FE) module is adopted to enhance the typicality and discriminability of node features, and to obtain richer and more representative features, which is helpful for facilitating accurate classification. Finally, additional constraints are added to the network model by introducing DCGAN, thus enhancing the robustness of the model. Through extensive empirical studies on several standard benchmarks, we find that DGCGAN outperforms state-of-the-art baselines on semi-supervised node classification and remote sensing image classification. [ABSTRACT FROM AUTHOR]

Change detection is a technique that can observe changes in the surface of the earth dynamically. It is one of the most significant tasks in remote sensing image processing. In the past few years, with the ability of extracting rich deep image features, the deep learning techniques have gained popularity in the field of change detection. In order to obtain obvious image change information, the attention mechanism is added in the decoder and output stage in many deep learning-based methods. Many of these approaches neglect to upgrade the ability of the encoders and the feature extractors to extract the representational features. To resolve this problem, this study proposes a deep multi-scale multi-attention siamese transformer network. A special contextual attention module combining a convolution and self-attention module is introduced into the siamese feature extractor to enhance the global representation ability. A lightly efficient channel attention block is added in the siamese feature extractor to obtain the information interaction among different channels. Furthermore, a multi-scale feature fusion module is proposed to fuse the features from different stages of the siamese feature extractor, and it can detect objects of different sizes and irregularities. To increase the accuracy of the proposed approach, the transformer module is utilized to model the long-range context in two-phase images. The experimental results on the LEVIR-CD and the CCD datasets show the effectiveness of the proposed network. [ABSTRACT FROM AUTHOR]

Panchromatic (PAN) and multispectral (MS) images have coordinated and paired spatial spectral information, which can complement each other and make up for their shortcomings for image interpretation. In this article, a novel classification method called the deep group spatial–spectral attention fusion network is proposed for PAN and MS images. First, the MS image is processed by unpooling to obtain the same resolution as that of the PAN image. Second, the group spatial attention and group spectral attention modules are proposed to extract image features. The PAN and the processed MS images are regarded as the input of the two modules, respectively. Third, the features from the previous step are fused by the attention fusion module, which aims to fully fuse multilevel features, take into account both the low-level features and the high-level features, and maintain the global abstract and local detailed information of the pixels. Finally, the fusion feature is fed into the classifier and the resulting map is obtained by pixel level. Extensive experiments and analysis on four datasets show that the proposed method achieves comparable results. [ABSTRACT FROM AUTHOR]

With the increase of the ageing in the world's population, the ageing and degeneration studies of physiological characteristics in human skin, bones, and muscles become important topics. Research on the ageing of bones, especially the skull, are paid much attention in recent years. In this study, a novel deep learning method representing the ageing-related dynamic attention (ARDA) is proposed. The proposed method can quantitatively display the ageing salience of the bones and their change patterns with age on lateral cephalometric radiographs images (LCR) images containing the craniofacial and cervical spine. An age estimation-based deep learning model based on 14142 LCR images from 4 to 40 years old individuals is trained to extract ageing-related features, and based on these features the ageing salience maps are generated by the Grad-CAM method. All ageing salience maps with the same age are merged as an ARDA map corresponding to that age. Ageing salience maps show that ARDA is mainly concentrated in three regions in LCR images: the teeth, craniofacial, and cervical spine regions. Furthermore, the dynamic distribution of ARDA at different ages and instances in LCR images is quantitatively analyzed. The experimental results on 3014 cases show that ARDA can accurately reflect the development and degeneration patterns in LCR images. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has become a mainstream method of hyperspectral image (HSI) classification. Many DL-based methods exploit spatial-spectral features to achieve better classification results. However, due to the complex backgrounds in HSIs, existing methods usually show unsatisfactory performance for the class pixels located on the land-cover category boundary area. In large part, this is because the network is susceptible to interference by the irrelevant information around the target pixel in the training stage, resulting in inaccurate feature extraction. In this article, a new multibranch transformer architecture (spectral spatial transformer (SST)-M) that assembles spatial attention and extracts spectral features is proposed to address this problem. The transformer model has a global receptive field and thus can integrate global spatial position information in the HSI cube. Meanwhile, we design a spatial sequence attention model to enhance the useful spatial location features and weaken invalid information. Considering that HSIs contain considerable spectral information, a spectral feature extraction model is designed to extract discriminative spectral features, replacing the widely used principal component analysis (PCA) method and obtaining better classification results than it. Finally, inspired by semantic segmentation, a mask prediction model is designed to classify all of the pixels in the HSI cube; this guides the neural network to learn precise pixel characteristics and spatial distributions. To verify the effectiveness of our algorithm (SST-M), quantitative experiments were conducted in three well-known datasets, namely, Indian Pines (IP), University of Pavia (PU), and Kennedy Space Center (KSC). The experimental results demonstrate that the proposed model achieves better performance than the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep descriptors have advantages over handcrafted descriptors on local image patch matching. However, due to the complex imaging mechanism of remote sensing images and the significant differences in appearance between multi-modal images, existing deep learning descriptors are unsuitable for multi-modal remote sensing image registration directly. To solve this problem, this article proposes a deep feature correlation learning network (Cnet) for multi-modal remote sensing image registration. First, Cnet builds a feature learning network based on the deep convolutional network with the attention learning module, to enhance feature representation by focusing on meaningful features. Second, this article designs a novel feature correlation loss function for Cnet optimization. It focuses on the relative feature correlation between matching and nonmatching samples, which can improve the stability of network training and decrease the risk of overfitting. In addition, the proposed feature correlation loss with a scale factor can further enhance network training and accelerate network convergence. Extensive experimental results on image patch matching (Brown, HPatches), cross-spectral image registration (VIS-NIR), multi-modal remote sensing image registration, and single-modal remote sensing image registration have demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these methods because of the significant spectral shift between target domain and source domain. Considering above issues, we propose a new method without requiring external dataset through combining a Generative Adversarial Network, Transformer Encoder and convolution block in a unified framework. The proposed method has both a global receptive field provided by Transformer Encoder and a local receptive field provided by convolution block. Experiments conducted on Indian Pines, PaviaU and KSC datasets demonstrate that our method exceeds the results of existing deep learning methods for hyperspectral image classification in the few-shot learning problem. [ABSTRACT FROM AUTHOR]

Remote sensing object tracking (RSOT) is a novel and challenging problem due to the negative effects of weak features and background noise. In this article, from the perspective of attention-focus deep learning, we propose a joint Siamese attention-aware network (JSANet) for efficient remote sensing tracking which contains both the self-attention and cross-attention modules. First, the self-attention modules we propose emphasize the interdependent channel-wise coefficient via channel attention and conduct corresponding space transformation of spatial domain information with spatial attention. Second, the cross-attention is designed to aggregate rich contextual interdependencies between the Siamese branches via channel attention and excavate association produces reliable correspondence with spatial attention. In addition, a composite feature combine strategy is designed to fuse multiple attention features. Experimental results on the Jilin-1 satellite video datasets demonstrate that the proposed JSANet achieves state-of-the-art performance in terms of precision and success rate, demonstrating the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Outlier detection is to separate anomalous data from inliers in the dataset. Recently, the most deep learning methods of outlier detection leverage an auxiliary reconstruction task by assuming that outliers are more difficult to recover than normal samples (inliers). However, it is not always true in deep auto-encoder (AE) based models. The auto-encoder based detectors may recover certain outliers even if outliers are not in the training data, because they do not constrain the feature learning. Instead, we think outlier detection can be done in the feature space by measuring the distance between outliers’ features and the consistency feature of inliers. To achieve this, we propose an unsupervised outlier detection method using a memory module and a contrastive learning module (MCOD). The memory module constrains the consistency of features, which merely represent the normal data. The contrastive learning module learns more discriminative features, which boosts the distinction between outliers and inliers. Extensive experiments on four benchmark datasets show that our proposed MCOD performs well and outperforms eleven state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Due to the scarcity and high cost of labeled hyperspectral image (HSI) samples, many deep learning methods driven by massive data cannot achieve the intended expectations. Semi-supervised and self-supervised algorithms have advantages in coping with this phenomenon. This paper primarily concentrates on applying self-supervised strategies to make strides in semi-supervised HSI classification. Notably, we design an effective and a unified self-supervised assisted semi-supervised residual network (SSRNet) framework for HSI classification. The SSRNet contains two branches, i.e., a semi-supervised and a self-supervised branch. The semi-supervised branch improves performance by introducing HSI data perturbation via a spectral feature shift. The self-supervised branch characterizes two auxiliary tasks, including masked bands reconstruction and spectral order forecast, to memorize the discriminative features of HSI. SSRNet can better explore unlabeled HSI samples and improve classification performance. Extensive experiments on four benchmarks datasets, including Indian Pines, Pavia University, Salinas, and Houston2013, yield an average overall classification accuracy of 81.65%, 89.38%, 93.47% and 83.93%, which sufficiently demonstrate that SSRNet can exceed expectations compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Semantic segmentation is an important yet unsolved problem in aerial scenes understanding. One of the major challenges is the intense variations of scenes and object scales. In this article, we propose a novel multi-scale aware-relation network (MANet) to tackle this problem in remote sensing. Inspired by the process of human perception of multi-scale (MS) information, we explore discriminative and diverse MS representations. For discriminative MS representations, we propose an inter-class and intra-class region refinement (IIRR) method to reduce feature redundancy caused by fusion. IIRR utilizes the refinement maps with intra-class and inter-class scale variation to guide MS fine-grained features. Then, we propose multi-scale collaborative learning (MCL) to enhance the diversity of MS feature representations. The MCL constrains the diversity of MS feature network parameters to obtain diverse information. Also, the segmentation results are rectified according to the dispersion of the multilevel network predictions. In this way, MANet can learn MS features by collaboratively exploiting the correlation among different scales. Extensive experiments on image and video datasets, which have large-scale variations, have demonstrated the effectiveness of our proposed MANet. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification. [ABSTRACT FROM AUTHOR]

With the development of the imaging technology of various sensors, multisource image classification has become a key challenge in the field of image interpretation. In this article, a novel classification method, called the deep multiview union learning network (DMULN), is proposed to classify multisensor data. First, an associated feature extractor is designed to process the multisource data by canonical correlation analysis (CCA) in the head of the network. Second, an improved deep learning architecture with two branches is presented to extract high-level view features from the associated features. Third, a novel pooling, called view union pooling, is proposed to fuse the multiview feature from the deep model. Finally, the fused feature is fed into the classifier. The proposed framework is easy to optimize since it is an end-to-end network. Extensive experiments and analysis on the datasets $IEEE\_{}grss\_{}dfc\_{}2017$ and $IEEE\_{}grss\_{}dfc\_{}2018$ show that the proposed method achieves comparable results. Our results demonstrate that abundant multisource information can improve the classification performance. [ABSTRACT FROM AUTHOR]

Deep learning methods have gained rapid development in hyperspectral pansharpening (HP) due to powerful spatial–spectral feature extraction ability. However, most of these methods are optimized using a single reconstruction objective. It is difficult for these methods to find a balance between spectral preservation and spatial preservation. Furthermore, these methods adopt interpolation or convolution to upsample the hyperspectral images (HSIs), which tends to cause noticeable spectral distortion. To conquer these issues, a novel multiobjective guided divide-and-conquer network (MO-DCN) is proposed for HP. It consists of a deconvolution long short-term memories (LSTMs) network (DLSTM) and a divide-and-conquer network (DCN). DLSTM leverages bi-direction learning to upsample HSIs by considering 3-D spatiotemporal dependencies. Then, DCN designs a two-branch architecture to reconstruct spatial and spectral information from upsampled HSIs and panchromatic images (PANIs), respectively, where the spatial branch designs an attention-in-attention module (AIAM) to emphasize complementary attention in a coarse-to-fine way. Finally, co-improvement of spatial and spectral information is formulated as an Epsilon-constraint-based multiobjective optimization. The Epsilon constraint method transforms one objective into a constraint and regards it as a penalty bound to make an excellent tradeoff between different objectives. Experimental results demonstrated that the proposed method markedly improves pansharpening performance in both the spatial and spectral domains and has superior fusion performance than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Change detection, as an important task of remote sensing image processing, has a wide range of applications in many aspects such as land use and natural disaster assessment. Recent change detection methods have achieved good results. However, due to the environmental difference between the bi-temporal images and the complicated imaging condition, there are usually problems such as missing small objects, incomplete objects, and rough edges in the change detection results. The existing change detection methods usually lack attention in these areas. In this paper, we propose a Siamese change detection method, named SMD-Net, for bi-temporal remote sensing change detection. The proposed model uses multi-scale difference maps to enhances the information of the changed areas step by step in order to have better change detection results. Furthermore, we propose a Siamese residual multi-kernel pooling module (SRMP) for high-level features to enhance the high-level change information of the model. For the low-level features of multiple skip connections, we propose a feature difference module (FDM) that uses feature difference to fully extract the change information and help the model generate more accurate details. The experimental results of our method on three public datasets show that compared with other benchmark methods, our network comprises better effectiveness and has a better trade-off between accuracy and calculation cost. [ABSTRACT FROM AUTHOR]

The excellent performance of graph convolutional networks (GCNs) on non-Euclidean data has drawn widespread attention from the hyperspectral image classification (HSIC) community, where the predefined graph (including node modeling and adjacency matrix calculation) plays a key role. However, existing GCN-based methods rely on manual efforts in constructing and updating graphs, and the superpixel-based node features lack high-level semantics. In this article, we propose an automatic graph learning convolutional network (Auto-GCN), which unifies the graph learning and HSIC in a “network-in-network” manner. Specifically, the graph is employed to model the interaction of the high-order tensors. Considering the powerful learning and representation capabilities of convolutional neural networks (CNNs), the semisupervised Siamese network (SiamNet) is embedded into GCNs and HSIC networks to accomplish the automatic learning and dynamic updating of the graph. GCNs further encode and infer the dynamic graph, and then, the learnable graph reprojection matrix is designed to assign graph representations to pixels. The dynamic graph serves the HSIC task during forward propagation, while the HSIC task continuously corrects the graph during backward propagation. Therefore, the “automatic” of the proposed Auto-GCN is not only reflected in the fact that the graph representation is designed and updated by an end-to-end network but is also HSIC task-oriented. The experimental results show that the proposed Auto-GCN outperforms other state-of-the-art methods on four publicly available hyperspectral datasets. [ABSTRACT FROM AUTHOR]

Recently, multicategory object detection in high-resolution remote sensing images is still a challenge. First, objects with significant scale differences exist in one scene simultaneously, so it is generally difficult for the detectors to balance the detection performance of large and small objects. Second, because of the complex background and the objects’ densely distributed characteristics in the remote sensing images, the extracted features usually have noise and blurred boundaries, which interfere with the detection performance of the object detectors. With this observation, we propose an end-to-end scale-aware network called feature split–merge–enhancement network (SME-Net) for remote sensing object detection, composed of the feature split-and-merge (FSM) module, the offset-error rectification (OER) module, and the object saliency enhancement (OSE) strategy. FSM eliminates salient information of large objects to highlight the features of small objects in the shallow feature maps. It also transmits the effective detailed features of large objects to the deep feature maps, alleviating feature confusion between multiscale objects. OER corrects the inconsistency of the features spatial layout among the multilayer feature maps by the proposed offset loss, so as to achieve supervised elimination and transmission in FSM. OSE enhances the features of interests and suppresses the background information by the proposed membership function, thus preventing false detection and missed detection caused by noise and blurred boundaries. The effectiveness of the proposed algorithm has been verified on multiple datasets. Our code is available at: https://github.com/Momuli/SMENet.git [ABSTRACT FROM AUTHOR]

Supervised machine learning and deep learning methods perform well in hyperspectral image classification. However, hyperspectral images have few labeled samples, which make them difficult to be trained because supervised classification methods rely heavily on sample quantity and quality. Inspired by the idea of self-supervised learning, this article proposes a hyperspectral imagery classification algorithm based on contrast learning, which uses the information of abundant unlabeled samples to alleviate the problem of insufficient label information in hyperspectral data. The algorithm uses a two-stage training strategy. In the first stage, the model is pretrained in the way of self-supervised learning, using a large number of unlabeled samples combined with data enhancement to construct positive and negative sample pairs, and contrastive learning (CL) is carried out. The purpose is to enable the model to make judgments on positive and negative samples. In the second stage, based on the pretrained model, the features of the hyperspectral image are extracted for classification, and a small amount of labeled samples are used to fine-tune the features. Experiments show that the features extracted by self-supervised learning achieved improved results on downstream classification task. [ABSTRACT FROM AUTHOR]

Ship detection plays a significant role in the high-resolution remote sensing (HRRS) community, but it is a challenging task due to the complex contents within HRRS images and the diverse orientation of ships. Recently, with the development of deep learning, the performance of the HRRS ship detection model has been improved greatly. Most of them employ deep networks and complicate anchor mechanism to get well ship detection results. Nevertheless, this kind of combination limits the detection efficiency. To address this problem, a new approach named accurate and real-time rotational ship detector (AR2Det) is proposed in this article to detect ships without the anchor mechanism. Based on the extracted features by the feature extraction module (FEM) and the central information of ships, AR2Det adopts two simple modules, ship detector (SDet) and center detector (CDet), to generate and improve the detection results, respectively. AR2Det is efficient due to the simple postprocessing and the lightweight network. Also, AR2Det performs satisfactorily due to the effective generation and enhancement strategy of bounding boxes. The extensive experiments are conducted on a public HRRS image ship detection dataset HRSC2016. The promising results show that our method outperforms the state-of-the-art approaches in terms of both accuracy and speed. [ABSTRACT FROM AUTHOR]

With powerful feature representations, convolutional neural networks (CNNs) have produced tremendous achievements in image classification tasks and, typically, entail millions of labeled samples to train massive parameters. However, the sample labeling of synthetic aperture radar (SAR) images is extremely difficult, especially pixelwise labels, and has, sometimes, required field trips to accomplish labeling. Moreover, the inherent speckle noise may weaken the ability of networks to extract effective features from SAR images. In this article, we address these issues by labeling a few patchwise samples and propose Ridgelet-Nets with speckle reduction regularization for SAR image scene classification by combining deep learning with multiscale geometric analysis and statistical modeling of SAR images. First, we design Ridgelet-Nets with convolutional kernels constructed by ridgelet filters to reduce the training parameters and learn more discriminative features. Then, we embed speckle reduction regularization in the Ridgelet-Nets to restrain the influence of speckle noise and smooth the classification maps, in which the prior information of SAR image statistical modeling is introduced. Finally, we propose an adaptive SAR image scene classification framework based on an extended hierarchical visual semantic model, considering the differences in the structures and spatial relationships of different regions in the SAR images, particularly large-scale and complex scenes. Experimental results on real SAR images demonstrate that the proposed framework can achieve preferable classification performance using very limited labeled samples. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has become a hot topic in the research field of hyperspectral image (HSI) classification. However, with increasing depth and size of deep learning methods, its application in mobile and embedded vision applications has brought great challenges. In this article, we address a network architecture search (NAS)-guided lightweight spectral–spatial attention feature fusion network (LMAFN) for HSI classification. The overall architecture of the proposed network is guided by several conclusions of NAS, which achieves fewer parameters and lower computation cost with deeper network structure by exploiting multiscale Ghost grouped with efficient channel attention (ECA) module for adaptively adjusting the weights of different channels. It helps fully extract spectral–spatial discriminant features to avoid information loss of the dimension reduction operation. Specifically, a multilayer feature fusion method is proposed to extract the fusion information of the spectral–spatial features of each layer by considering complementary information of different hierarchical structures. Therefore, high-lever spectral–spatial attributes are gradually exploited along with the increase in layers and the fusion of layers. The experimental verification on three real HSI data sets demonstrates that the proposed framework presents more satisfying classification performance and efficiency with deeper network structure and lower parameter size. [ABSTRACT FROM AUTHOR]

The content-based remote sensing image retrieval (CBRSIR) has attracted increasing attention with the number of remote sensing (RS) images growing explosively. Benefiting from the strong capacity of the deep convolutional neural network (DCNN), the performance of CBRSIR has been improved in recent years. Although great successes have been obtained, learning the RS images’ representative features and enhancing the retrieval efficiency for the large-scale CBRSIR tasks are still two challenging problems. In this article, we propose a new CBRSIR method named feature and hash (FAH) learning, which consists of a deep feature learning model (DFLM) and an adversarial hash learning model (AHLM). The DFLM aims at learning the RS images’ dense features to guarantee the retrieval precision. In the DFLM, the DCNN and the proposed feature aggregation are integrated to capture the multiscale features. Then, the discrimination of the obtained features can be highlighted by the attention map in the developed attention branch. The AHLM maps the dense features onto the compact hash codes so that the retrieval efficiency can be improved. The AHLM contains a hash learning submodel and an adversarial regularization submodel. In particular, the hash learning submodel learns the real-valued hash codes that are similarity preserved by semantic supervisions. The adversarial regularization submodel regularizes the real-valued hash codes to learn the discrete uniform distribution with possible values 0 and 1. In this way, the hash codes are coding-balanced and the quantization errors are reduced. Encouraging experimental results counted on three public benchmark data sets demonstrate that our FAH can achieve competitive performance in the CBRSIR task compared with many existing hash learning methods. [ABSTRACT FROM AUTHOR]

In recent years, with the development of deep learning (DL), the hyperspectral image (HSI) classification methods based on DL have shown superior performance. Although these DL-based methods have great successes, there is still room to improve their ability to explore spatial–spectral information. In this article, we propose a 3-D octave convolution with the spatial–spectral attention network (3DOC-SSAN) to capture discriminative spatial–spectral features for the classification of HSIs. Especially, we first extend the octave convolution model using 3-D convolution, namely, a 3-D octave convolution model (3D-OCM), in which four 3-D octave convolution blocks are combined to capture spatial–spectral features from HSIs. Not only the spatial information can be mined deeply from the high- and low-frequency aspects but also the spectral information can be taken into account by our 3D-OCM. Second, we introduce two attention models from spatial and spectral dimensions to highlight the important spatial areas and specific spectral bands that consist of significant information for the classification tasks. Finally, in order to integrate spatial and spectral information, we design an information complement model to transmit important information between spatial and spectral attention features. Through the information complement model, the beneficial parts of spatial and spectral attention features for the classification tasks can be fully utilized. Comparing with several existing popular classifiers, our proposed method can achieve competitive performance on four benchmark data sets. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands for classification. Moreover, for patchwise-based CNN models, equal treatment of spatial information from the pixel-centered neighborhood also hinders the performance of these methods. In this article, we propose an end-to-end residual spectral–spatial attention network (RSSAN) for HSI classification. The RSSAN takes raw 3-D cubes as input data without additional feature engineering. First, a spectral attention module is designed for spectral band selection from raw input data by emphasizing useful bands for classification and suppressing useless bands. Then, a spatial attention module is designed for the adaptive selection of spatial information by emphasizing pixels from the same class as the center pixel or those are useful for classification in the pixel-centered neighborhood and suppressing those from a different class or useless. Second, two attention modules are also used in the following CNN for adaptive feature refinement in spectral–spatial feature learning. Third, a sequential spectral–spatial attention module is embedded into a residual block to avoid overfitting and accelerate the training of the proposed model. Experimental studies demonstrate that the RSSAN achieved superior classification accuracy compared with the state of the art on three HSI data sets: Indian Pines (IN), University of Pavia (UP), and Kennedy Space Center (KSC). [ABSTRACT FROM AUTHOR]

In order to better interpret polarimetric synthetic aperture radar (PolSAR) images, many scholars tend to do target decomposition for PolSAR images and utilize the obtained features to perform subsequent classification. These target decomposition features play an important role in terrain classification but completely utilizing them produces a high computational complexity. Furthermore, some features have a negative impact on the classification task. Therefore, selecting the appropriate amount of high-quality features is of great significance to the classification task. In this paper, we propose a convolutional neural network (CNN)-based feature selection algorithm for PolSAR image classification. First, we design a 1-D CNN for feature selection, then train the designed network with all the decomposition features to obtain a trained model. Second, the Kullback–Leibler distance (KLD) between different features is utilized as a standard to select feature subsets. Third, feature subsets with excellent performance form the final results. Due to the special structure of the 1-D CNN, repetitively training model is avoided when the input changes. Different from traditional feature selection methods, our method considers the performance of features combination rather than single feature contribution. To this end, the feature subsets selected by the proposed method are more useful to the classification task. Innovatively introducing KLD in the selection stage avoids random selection and improves the selection efficiency. Finally, we validate the performance of selected feature subsets in traditional and deep learning classification frameworks. Experiments demonstrate that features selected by the proposed method have a good performance comparing with others on three real PolSAR data sets. [ABSTRACT FROM AUTHOR]

Object detection has played an important role in the remote sensing (RS) image understanding domain, but it still suffers from challenges, including the complexity of the image background, the variety of objects and differences of target scales. Although many studies applying deep learning methods to object detection have been presented, effective methods of multiscale object detection in RS images are still lacking. In this paper, we propose a novel object detection approach based on the Multiscale-SELU-DenseNet (MSE-DenseNet) and the dynamic anchor assignment (DAA) strategy. We fuse a highly modified DenseNet and a feature pyramid network into the MSE-DenseNet to extract multi-level feature descriptions of RS images, and then we use them to detect objects on various scales. To further improve the detection performance, we propose a DAA strategy for assigning prior anchor boxes. The experimental results show that our proposed method outperforms state-of-the-art detection methods on the VHR-10 dataset and maintains a real-time detection speed. [ABSTRACT FROM AUTHOR]

Supervised deep neural networks (DNNs) have been extensively used in diverse tasks. Generally, training such DNNs with superior performance requires a large amount of labeled data. However, it is time-consuming and expensive to manually label the data, especially for tasks in remote sensing, e.g., change detection. The situation motivates us to resort to the existing related images with labels, from which the concept of change can be adapted to new images. However, the distributions of the related labeled images (source domain) and unlabeled new images (target domain) are similar but not identical. It impedes a change detection model learned from source domains being well applied to the target domain. In this paper, we propose a transferred deep learning-based change detection framework to solve this problem. It consists of pretraining and fine-tuning stages. In the pretraining process, we propose two tasks to be learned simultaneously, namely, change detection for the source domain with labels and reconstruction of the unlabeled target data. The auxiliary task aims to reconstruct the difference image (DI) for the target domain. DI is an effective feature, such that the auxiliary task is of much relevance to change detection. The lower layers are shared between these two tasks in the training process. It mitigates the distribution discrepancy between the source and target domains and makes the concept of change from the source domain adapt to the target domain. In addition, we evaluate three modes of the U-net architecture to merge the information for a pair of patches. To fine-tune the change detection network (CDN) for the target domain, two strategies are exploited to select the pixels that have a high possibility of being correctly classified by an unsupervised approach. The proposed method demonstrates an excellent capacity for adapting the concept of change from the source domain to the target domain. It outperforms the state-of-the-art change detection methods via experimental results on real remote sensing data sets. [ABSTRACT FROM AUTHOR]

With the rapid development of various technologies of satellite sensor, synthetic aperture radar (SAR) image has been an import source of data in the application of change detection. In this paper, a novel method based on a convolutional neural network (CNN) for SAR image change detection is proposed. The main idea of our method is to generate the classification results directly from the original two SAR images through a CNN without any preprocessing operations, which also eliminate the process of generating the difference image (DI), thus reducing the influence of the DI on the final classification result. In CNN, the spatial characteristics of the raw image can be extracted and captured by automatic learning and the results with stronger robustness can be obtained. The basic idea of the proposed method includes three steps: it first produces false labels through unsupervised spatial fuzzy clustering. Then we train the CNN through proper samples that are selected from the samples with false labels. Finally, the final detection results are obtained by the trained convolutional network. Although training the convolutional network is a supervised learning fashion, the whole process of the algorithm is an unsupervised process without priori knowledge. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of our algorithm in simulated and real data sets. In addition, we try to apply our algorithm to the change detection of heterogeneous images, which also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods have been widely used for the classification of hyperspectral images (HSIs). However, the training of deep models is very time-consuming. In addition, the rare labeled samples of remote sensing images also limit the classification performance of deep models. In this letter, a simple deep learning model, a rotation-based deep forest (RBDF), is proposed for the classification of HSIs. Specifically, the output probability of each layer is used as the supplement feature of the next layer. The rotation forest is used to increase the discriminative power of spectral features and neighboring pixels are used to introduce spatial information. The RBDF consumes much less training time than traditional deep models. Experimental results based on three HSIs demonstrate that the proposed method achieves the state-of-the-art classification performance. In addition, the RBDF obtains satisfied classification results with very few training samples. [ABSTRACT FROM AUTHOR]

In very recent years, deep learning based methods have been widely introduced for the classification of hyperspectral images (HSI). However, these deep models need lots of training samples to tune abundant parameters which induce a heavy computation burden. Therefore, most of these algorithms need to be accelerated with high-performance graphics processing units (GPU). In this paper, a new deep model-densely connected deep random forest (DCDRF) is proposed to classify the hyperspectral images. This model is composed of multiple forward connected random forests. The DCDRF has following merits: 1) It obtains satisfactory classification accuracy with a small number of training samples, 2) It can be run efficiently on the central processing unit (CPU), 3) Only a few parameters are involved during the training. Experimental results based on three hyperspectral images demonstrate that the proposed method can achieve better classification performance than the conventional deep learning based methods. [ABSTRACT FROM AUTHOR]

The extraction of joint spatial–spectral features has been proved to improve the classification performance of hyperspectral images (HSIs). Recently, utilizing convolutional neural networks (CNNs) to learn joint spatial–spectral features has become of great interest. However, the existing CNN models ignore complementary spatial–spectral information among the shallow and deep layers. Moreover, insufficient training samples in HSIs afflict these CNN models with overfitting problem. In order to address these problems, a novel CNN method for HSI classification is proposed. It considers multilayer spatial–spectral feature fusion and sample augmentation with local and nonlocal constraints, which is abbreviated as MSLN-CNN. In MSLN-CNN, a triple-architecture CNN is constructed to extract spatial–spectral features by cascading spectral features to dual-scale spatial features from shallow to deep layers. Then, multilayer spatial–spectral features are fused to learn complementary information among the shallow layers with detailed information and the deep layers with semantic information. Finally, the multilayer spatial–spectral feature fusion and classification are integrated into a unified network, and MSLN-CNN can be optimized in the end-to-end way. To alleviate the small sample size problem, the unlabeled samples having high confidences on local spatial constraint and nonlocal spectral constraint are selected and prelabeled. The nonlocal spectral constraint considers the structure information with spectrally similar samples in the nonlocal searching, while the local spatial constraint utilizes the contextual information with spatially adjacent samples. Experimental results on several hyperspectral datasets demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods, especially with the limited training samples. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a fast unsupervised deep fusion framework for change detection of multitemporal synthetic aperture radar (SAR) images is presented. It mainly aim at generating a difference image (DI) in the feature learning procedure by stacked auto-encoders (SAEs). Stacked auto-encoders, as one kind of deep neural network, can learn feature maps that retain the structural information but suppress the noise in the SAR images, which will be beneficial for DI generation. Compared with shallow network, the proposed framework can extract more available features, and be favorable for getting better change results. Different with other common deep neural networks, our proposed method does not need labeled data to train the network. In addition, we find a subset of the entire samples that appropriately represent the whole dataset to speed up the training of the deep neural network without under-fitting. Moreover, we design a fusion network structure that can combine ratio operator based method to ensure that the representations of higher layers are better than that of the lower ones. To summarize, the main contribution of our work lies in using of deep fusion network for generation of DI in a fast and unsupervised way. Experiments on four real SAR images confirm that our network performs better than traditional ratio methods and convolutional neural network. [ABSTRACT FROM AUTHOR]

In hyperspectral image processing, classification is one of the most popular research topics. In recent years, research progress made in deep-learning-based hierarchical feature extraction and classification has shown a great power in many applications. In this paper, we propose a novel local spatial sequential (LSS) method, which is used in a recurrent neural network (RNN). Using this model, we can extract local and semantic information for hyperspectral image classification. First, we extract low-level features from hyperspectral images, including texture and differential morphological profiles. Second, we combine the low-level features together and propose a method to construct the LSS features. Afterwards, we build an RNN and use the LSS features as the input to train the network for optimizing the system parameters. Finally, the high-level semantic features generated by the RNN is fed into a softmax layer for the final classification. In addition, a nonlocal spatial sequential method is presented for the recurrent neural network model (NLSS-RNN) to further enhance the classification performance. NLSS-RNN finds nonlocal similar structures to a given pixel and extracts corresponding LSS features, which not only preserve the local spatial information, but also integrate the information of nonlocal similar samples. The experimental results on three publicly accessible datasets show that our proposed method can obtain competitive performance compared with several state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Deep learning has been well-known for a couple of years, and it indicates incredible possibilities for unsupervised learning of representations with the clustering algorithm. The forms of Convolution Neural Networks (CNN) are now state-of-the-art for many recognition and clustering tasks. However, with the perpetual incrementation of digital images, there exist more and more redundant, irrelevant, and noisy samples which cause CNN running to gradually decrease, and its clustering accuracy decreases concurrently. To conquer these issues, we proposed an effective clustering method for a large-scale image dataset which combines CNN and a Fuzzy-Rough C-Mean (FRCM) clustering algorithm. The main idea is that first a high-level representation, learned by multi-layers of CNN with one clustering layer, produce the initial cluster center, then during training image clusters, and representations, are updating jointly. FRCM is utilized to update the cluster centers in the forward pass, while the parameters of proposed CNN are updated by the backward pass based on Stochastic Gradient Descent (SGD). The concept of the rough set of lower and boundary approximations deal with uncertainty, vagueness, and incompleteness in cluster definition, and fuzzy sets enable efficient handling of overlapping partitions in the noisy environment. The experiment results show that the proposed FRCM based unsupervised CNN clustering method is better than the standard K-Mean, Fuzzy C-Mean, FRCM and also other deep-learning-based clustering algorithms on large-scale image data. [ABSTRACT FROM AUTHOR]

Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and has also been used to solve SAR image classification problems. Deep learning algorithms typically require a large amount of training data to achieve high accuracy. In contrast, the size of SAR image datasets is often comparatively limited. Therefore, this paper proposes a novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples. Based on the convolutional neural networks (CNN), M-Net adds an information recorder to remember and store samples’ spatial features, and then it uses spatial similarity information of the recorded features to predict unknown sample labels. M-Net's use of this information recorder may cause difficulties for convergence if conventional CNN training methods were directly used to train M-Net. To overcome this problem, we propose a transfer parameter technique to train M-Net in two steps. The first step is to train a CNN, which has the same structure as the part of CNN incorporated in M-Net, to obtain initial training parameters. The second step applies the initialized parameters to M-Net and then trains the entire M-Net. This two-step training approach helps us to overcome the nonconvergence issue, and also reduces training time. We evaluate M-Net using the public benchmark MSTAR dataset, and achieve higher accuracy than several other well-known SAR image classification algorithms. [ABSTRACT FROM AUTHOR]

For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral data sets, and the results show that our proposed method has a better performance. [ABSTRACT FROM PUBLISHER]

The graph model-based semisupervised machine learning is well established. However, its computational complexity is still high in terms of the time consumption especially for large data. In this letter, we propose a fast semisupervised classification algorithm using the recently presented spatial-anchor graph for a large polarimetric synthetic aperture radar (Pol-SAR) data, named as Fast Spatial-Anchor Graph (FSAG) based algorithm. Based on an initial superpixel segmentation on the PolSAR image, the homogenous regions are obtained. The border pixels are reassigned to the most similar superpixel according to majority voting and distance measurement. Then, feature vectors are weighted within local homogenous regions. The refined spatial-anchor graph is constructed with these regions, and the semisupervised classification is conducted. Experimental results on synthesized and real PolSAR data indicate that the proposed FSAG greatly reduces time consumption and maintains the accuracy for terrain classifications compared with state-of-the-art graph-based approaches. [ABSTRACT FROM PUBLISHER]

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a selectable Transformer and Gist CNN network (STGC-Net). It designs a subspace similar recombination module (SSR-Module) based on non-negative matrix factorization (NMF) and the self-attention mechanism for feature decomposition. This can alleviate the redundant information of multi-modal data and extract their own singular and common features. Considering that the MS and the PAN images exhibit different advantageous properties, a selectable self-attention spectral feature extraction module (S 3 FE-Module) and a multi-stream Gist spatial feature extraction module (MGSFE-Module) are proposed for the different singular features. The former can refine the Transformer's input and simultaneously characterize the sequence information between channels for the MS image. The latter introduces the positional relationship between local features while extracting spatial features for the PAN image, thereby improving the accuracy of scene classification. Experimental results indicate that the proposed method performs better than the other methods. The relevant code of this paper is provided at: https://github.com/ru-willow/ST-GC-Net. • A subspace similar recombination module is proposed. • The selectable self-attention spectral feature extraction module is designed. • The multi-stream Gist spatial feature extraction module is introduced. [ABSTRACT FROM AUTHOR]

As an advanced nonlinear technique, deep learning, which is based on deep neural networks (DNNs), has attracted considerable attentions. In this paper, we propose a novel neighborhood preserved deep neural network (NPDNN) for polarimetric synthetic aperture radar feature extraction and classification. The spatial relation between pixels is exploited by a jointly weighting strategy. Not only the spatial neighbors but also the pixels in the same superpixel are utilized to weight each pixel. This strategy maintains the spatial dependence leading to superior homogeneity of the terrains without extra computational memory. Moreover, a few labeled samples and their nearest neighbors are employed to train the multilayer NPDNN, which preserves the local structure and reduces the number of labeled samples for classification. Experimental results on synthesized and real PolSAR data show that the proposed NPDNN can improve the classification accuracy compared with state-of-the-art DNNs despite a few input samples. [ABSTRACT FROM PUBLISHER]

The deep learning algorithm has made great breakthroughs in optical image processing. Some deep learning algorithms require a large number of labeled samples for training. For PolSAR data sets, due to the influence of speckle noise and other factors, high-quality labeled data are limited. Therefore, it is meaningful to use deep learning algorithm to solve PolSAR classification problem in limited labeled dataset. This paper proposes a spatial feature-based convolutional neural network (SF-CNN). The network adopts a dual-branch CNN structure. Both of the two branches have the same structure and share parameters. SF-CNN can receive more than one sample as input. SF-CNN's special structure can expand the original training set by combining different samples, and alleviate the problem of insufficient labeled training data in PolSAR image classification tasks. When training, SF-CNN maps high-dimensional PolSAR image to low-dimensional feature space. In low-dimensional feature space, SF-CNN enhances the ability of network to extract discriminative features by maximizing or minimizing the distance between feature centers of different classes. In order to dig up the relationship between the samples, the test sample features are compared with every training sample feature when testing. Finally, labels of test samples are determined by the comparison result. The result of SF-CNN in PolSAR image classification task is better than that of standard CNN. • This paper proposes a spatial feature-based convolutional neural network (SF-CNN). • SF-CNN adopts a dual-branch CNN structure. • Both of the two branches have the same structure and share parameters. • SF-CNN can receive more than one sample as input. • SF-CNN's special structure can expand the original training set. [ABSTRACT FROM AUTHOR]

In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification (ESC) models cannot handle new signal types. In this context, the emergence of class-incremental learning aims to incrementally update the classification model as new categories emerge. In this paper, an electromagnetic signal classification framework based on class exemplar selection and a multi-objective linear programming classifier (CES-MOLPC) is proposed in order to continuously learn new classes in an incremental manner. Specifically, our approach involves the adaptive selection of class exemplars considering normalized mutual information and a multi-objective linear programming classifier. The former is used to maintain the classification capability of the model for previous categories by selecting key samples, while the latter is used to allow the model to adapt quickly to new categories. Meanwhile, a weighted loss function based on cross-entropy and distillation loss is presented in order to fine-tune the model. We demonstrate the effectiveness of the proposed CES-MOLPC method through extensive experiments on the public RML2016.04c data set and the large-scale real-world ACARS signal data set. The results of the comparative experiments demonstrate that our method can achieve significant improvements over state-of-the-art methods. [ABSTRACT FROM AUTHOR]