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Remote sensing image super-resolution (SR) aims to boost the image resolution while recovering rich high-frequency details. Currently, most of the SR methods are based on an assumption that the degradation kernel is a specific downsampler. However, the degradation kernel is unknown and sophisticated for real remote sensing scenes, leading to a severe performance drop. To alleviate this problem, we propose a multilayer degradation representation-guided blind SR method for remote sensing images, which mainly consists of three key steps. First, an unsupervised representation learning is exploited to learn the degradation representation from low-resolution images. Then, a degradation-guided deep residual module is designed to model high-order features across different scales from the original images. Finally, a multilayer degradation-aware feature fusion mechanism is proposed to restore the finer details. Experiments on synthetic and real datasets demonstrate that the proposed method can achieve promising performance with respect to other state-of-the-art SR approaches. [ABSTRACT FROM AUTHOR]

Spatio-temporal fusion by combining the complementary spatial and temporal advantages of multisource remote-sensing images to obtain time-series high spatial resolution images is highly desirable in monitoring surface dynamics. Currently, deep learning (DL)-based fusion methods have received extensive attention. However, existing DL-based spatio-temporal fusion methods are generally limited in fusing the images with land cover changes. In this article, we propose a spatio-temporal–spectral collaborative learning framework for spatio-temporal fusion to alleviate this problem. Specifically, the proposed method integrates the convolutional neural network and recurrent neural network into a unified framework, consisting of three subnetworks: the multiscale Siamese convolutional neural network (MSCNN), the multilayer convolutional recurrent neural network, and the adaptive weighting fusion network (AWFNet). The MSCNN has a flexible weight-sharing network to extract multiscale spatial–spectral features from multisource remote-sensing images. The multilayer convolutional recurrent neural network is constructed on the convolutional long short-term memory units to comprehensively learn the land cover changes by spatial, spectral, and temporal joint features. The AWFNet with a spatio-temporal–spectral change (STSC) loss is proposed to further improve the interpretability and robustness. The experiments were performed on the publicly available benchmark datasets featured phenology and land cover type changes, respectively. The experimental results demonstrated the competitive performance of the proposed method than other state-of-the-art fusion methods. [ABSTRACT FROM AUTHOR]

Pansharpening is a fundamental and hot-spot research topic in remote sensing image fusion. In recent years, self-attention-based transformer has attracted considerable attention in natural language processing (NLP) and introduced to attend to computer vision (CV) tasks. Inspired by great success of the vision transformer (ViT) in image classification, we propose an improved and advanced purely transformer-based model for pansharpening. In the proposed method, stacked multispectral (MS) and panchromatic (PAN) images are cropped into patches (i.e., tokens), and after a three-layer self-attention-based encoder, these tokens contain rich information. After upsampled and stitched, a high spatial resolution (HR) MS image is finally obtained. Instead of convolutional neural networks (CNNs) pursuing a short-distance dependency, our proposed method aims to build up a long-distance dependency, to make full use of more useful features. The experiments were conducted on an opening benchmark dataset, including IKONOS with four-band MS/PAN images and WorldView-2 MS images featured by eight bands. In addition, the experiments were performed on reduced and full-resolution datasets from both qualitative and quantitative evaluation aspects. The experimental results indicate the competitive performance of the proposed model than other pansharpening methods, including the state-of-the-art pansharpening algorithms based on CNN. [ABSTRACT FROM AUTHOR]

Spatio-temporal fusion aims to integrate multisource remote sensing images with complementary high spatial and temporal resolutions, so as to obtain time-series high spatial resolution fused images. Currently, deep learning (DL)-based spatio-temporal fusion methods have received broad attention. However, on one hand, most of the existing DL-based methods train the model in a band-by-band manner, ignoring the correlations among bands. On the other hand, the general coarse spatio-temporal changes in low spatial resolution images (e.g., MODIS) calculated at the pixel domain cannot completely cover the fine spatio-temporal changes in high spatial resolution images (e.g., Landsat), due to complex surface features and the general large spatial resolution ratio between fine and coarse images. Besides, the existing DL-based spatio-temporal fusion methods are insufficient in exploring multiscale information by only stacking convolutional kernels with different sizes. To alleviate the above challenges, we propose a progressive spatio-temporal attention fusion model in a multiband training manner based on generative adversarial network (PSTAF-GAN). Specifically, we design a flexible multiscale feature extraction architecture to extract multiscale feature hierarchies. Then, spatio-temporal changes are calculated on the feature domain in different feature hierarchies. Besides, a spatio-temporal attention fusion architecture is proposed to fuse the spatio-temporal changes and ground details in a coarse-to-fine manner, which can explore multiscale information more sufficient and gradually recover the target image. The results of quantitative and qualitative experiments on two publicly available benchmark datasets show that the proposed PSTAF-GAN can achieve the best performance compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Semisupervised semantic segmentation is an effective way to reduce the expensive manual annotation cost and take advantage of the unlabeled data for remote sensing (RS) image interpretation. Recent related research has mainly adopted two strategies: self-training and consistency regularization. Self-training tries to acquire accurate pseudo-labels to explicitly expand the train set. However, the existing methods cannot accurately identify false pseudo-labels, suffering from their negative impact on model optimization. The consistency regularization constrains the model by producing consistent predictions robust to the perturbations introduced in the sample or feature domain but requires a sufficient number of training data. Therefore, we propose a strategy for the semisupervised semantic segmentation of the RS images. The proposed model in the generative adversarial network (GAN) framework is optimized by consistency self-training, learning the distributions of both labeled and unlabeled data. The discriminator is optimized by accurate pixel-level training labels instead of the image-level ones, thereby assessing the confidence for the prediction of each pixel, which is then used to reweight the loss of the unlabeled data in self-training. The generator is optimized with the consistency constraint with respect to all random perturbations on the unlabeled data, which increases the sample diversity and prompts the model to learn the underlying distribution of the unlabeled data. Experimental results on the the large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) datasets and the International Society for Photogrammetry and Remote Sensing (ISPRS) datasets show that our framework outperforms several state-of-the-art semisupervised semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Exploring effective spectral–spatial feature extraction methods is one of the most focused problems in current hyperspectral image (HSI) classification research. However, complex spectral–spatial structure characteristics in HSIs, e.g., shape-variable spatial structure and nonlinear spectral structure, are difficult to be effectively extracted and jointly represented. To overcome this issue, a novel superpixel-based Brownian descriptor (SBD) method for HSI classification is proposed in this article. In specific, superpixel segmentation is first used to extract shape-adaptive spatial structure information from dimension-reduced HSI, leading to generate nonoverlapping homogeneous 3-D image blocks. Then, similar pixels within the 3-D image block are jointly represented by a new local spectral–spatial feature based on the Brownian descriptor (BD). This is the first time that the BD is introduced to measure both linear and nonlinear correlations among different spectral bands in HSI. On one hand, the integration of superpixel and BD helps to provide much richer and more valuable information for better discrimination between different categories. On the other hand, the SBD can effectively represent the internal structure characters within each 3-D image block of different spatial shapes by a symmetric positive definite matrix of a united form. Finally, considering that the SBD lies on the Riemannian manifold space, a log-Euclidean kernel sparse representation (LKSR) classifier is introduced to obtain the classification results. Experimental results on three widely used real hyperspectral datasets indicate the performance superiority of the proposed SBD method over several state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Coastal wetland monitoring plays an important role in the protection and restoration of ecosystems in this world. UAV-hyperspectral imaging, as an emerging technique for Earth observation and space exploration, provides the huge potential ability to identify different wetland species. In this work, a multilayer global spectral–spatial attention network (MGSSAN) is proposed for mapping coastal wetlands, which mainly consists of two major steps. First, a two-branch convolutional neural network (CNN) framework with residual connection is developed to obtain an initial classification probability map, in which one branch is used to capture the spectral information, the other branch is used to extract spatial information, and a global spectral–spatial attention module is designed to guide networks focusing on those features that are more discriminative. Second, an extended random walker method is utilized to optimize the initial classification probabilities, so as to yield the final map. Experiments performed on three wetland HSI datasets created by ourselves verify that the proposed method can obtain superior performance with respect to several state-of-the-art hyperspectral image classification methods. [ABSTRACT FROM AUTHOR]

Exploring fast and effective spectral-spatial feature extraction algorithms for hyperspectral image (HSI) classification is one of the most focus problems in current hyperspectral remote-sensing research. Generally, the size of homogeneous regions in HSIs is not consistent in real scenario and real scenario usually consist of ground objects of different scales. Multiscale strategy starts to be used to construct discriminative features at different scales for HSI classification in recent years. To efficiently characterize the multiscale spectral-spatial features of HSIs, a curvature filters-based multiscale feature extraction method with multiscale superpixel segmentation constraint is proposed. The proposed algorithm is composed of the following major stages. First, global multiscale spectral-spatial features are efficiently extracted via progressively curvature filtering and downsampling operations, which can be regarded as an image pyramid decomposition method. Next, a multiscale superpixel segmentation strategy is applied on the first layer of the image pyramid, and a weighted mean operation is applied within and among superpixels to extract the local multiscale spatial features (LMSFs). Finally, the global multiscale curvature features (GMCFs) and the superpixel segmentation-based LMSFs are fused to form the final multiscale spectral-spatial features for classification purposes. To verify the capabilities of the proposed method, comprehensive experiments are performed on five real hyperspectral datasets. Experimental results demonstrate that the proposed method can significantly improve the classification accuracies compared to several standard HSI feature extraction and classification methods, especially when the number of samples for training is limited. [ABSTRACT FROM AUTHOR]

Pansharpening methods have been developed for nearly 40 years; however, how to quantitatively evaluate the quality of pansharpened images at full resolution (FR) is probably the most debated topic in this field due to the inherent unavailable of the real HR MS reference image. In this article, a novel blind FR quality evaluation method for pansharpening is proposed. In the proposed method, spatial and spectral features that are sensitive to spatial and spectral distortions of fused images are comprehensively considered and jointly learned based on online multivariate Gaussian (MVG) to construct the evaluation model. It directly outputs the quality of fused images, rather than the stepwise evaluation of spectral score, spatial score, and final overall quality score by the weighted combination of them, which may introduce contradictory results. First, a pristine benchmark evaluation model is established on the spatial features from the original high-spatial-resolution (HR) panchromatic (PAN) image and the spectral invariant assumption between ideal fused and original multispectral (MS) images. Second, a testing evaluation model for the fused image is founded. Finally, the quality of the fused image is measured based on the distance between the testing and benchmark models. The experimental results demonstrated the superior performance of the proposed method. Furthermore, the proposed method can be generalized to other interesting tasks, such as the nonreference evaluation for pansharpening with missing information and the nonreference evaluation for hyperspectral image fusion. The source code is available on https://github.com/yyxhpkq/MQNR. [ABSTRACT FROM AUTHOR]

Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In general, existing retrieval methods can be regarded as visual-based retrieval approaches that search and return a set of similar images to a given query image from a database. Although these retrieval methods have delivered good results, there is still a question that needs to be addressed: can we obtain the accurate semantic labels of the returned similar images to further help analyzing and processing imagery? To this end, in this article, we redefine the image retrieval problem as visual and semantic retrieval of images. Especially, we propose a novel deep hashing convolutional neural network (DHCNN) to retrieve similar images and classify their semantic labels simultaneously in a unified framework. In more detail, a convolutional neural network (CNN) is used to extract high-dimensional deep features. Then, a hash layer is perfectly inserted into the network to transfer the deep features into compact hash codes. In addition, a fully connected layer with a softmax function is performed on the hash layer to generate the probability distribution of each class. Finally, a loss function is elaborately designed to consider the label loss of each image and similarity loss of pairs of images simultaneously. Experimental results on three remote sensing data sets demonstrate that the proposed method can achieve state-of-art retrieval and classification performance. [ABSTRACT FROM AUTHOR]

Most of the existing hyperspectral anomaly detectors are designed based on a single pixel-level feature. These detectors may not adequately utilize spectral–spatial information in hyperspectral images (HSIs) for detecting anomalies. To overcome this problem, this article introduces a novel subpixel-pixel-superpixel guided fusion (SPSGF) method for hyperspectral anomaly detection. This approach comprises three main steps. First, subpixel-, pixel-, and superpixel-level features are extracted from an HSI by employing the spectral unmixing, morphological operation, and superpixel segmentation techniques, respectively. Then, based on the spatial consistency of three features, a guided filtering-based weight optimization technique is developed to construct weight maps for fusion. Finally, a simple yet effective decision fusion method is adopted to utilize the complemental information of three features, and then generates a fused detection result. The performance of the proposed approach is evaluated on three real-scene HSIs and one synthetic HSI. Experimental results validate the advantages of the SPSGF method. [ABSTRACT FROM AUTHOR]

Active learning (AL) attempts to actively select the most representative or useful training samples in an iterative manner. The aim is to simultaneously improve the classification performance and reduce the manual labeling effort. In this article, a novel subpixel-pixel-superpixel-based multiview AL (MAL) (SPS-MAL) method is proposed for hyperspectral image (HSI) classification. Here, the multiple views are generated via extracting the subpixel-level, pixel-level, and superpixel-level information. The multiple views can reflect various characteristics of HSI, i.e., spectral mixture, spectral discrimination, and spectral–spatial structure. Therefore, the joint use of diverse and complementary information in multiple views will contribute to a better identification ability of different classes. In addition, a coarse-to-fine MAL algorithm is introduced to effectively select the most representative samples with the most uncertainty. Specifically, a disagreement analysis on multiple views and joint posterior probability estimation is used to query unlabeled samples. Along with the expansion of training samples, view-specific confidence scores are estimated to adaptively integrate the classification results of multiple views, according to their discrimination performance. In this way, the classification accuracy will be further boosted while the number of necessary training samples can be significantly reduced. The experimental classification results on three well-known HSIs demonstrate the effectiveness of the proposed SPS-MAL method. [ABSTRACT FROM AUTHOR]

Spectral–spatial Gabor filtering, which is based on 3-D local harmonic analysis, has been a powerful spectral–spatial feature extraction tool for hyperspectral image (HSI) classification. However, existing spectral–spatial Gabor approaches are prone to oversmoothing, neglecting the existences of edges and negatively affecting the classification. In this article, we propose a new HSI Gabor filtering concept, called spectral–spatial-range Gabor filtering, which intends to restrain edge interference from disturbing local spectral–spatial harmonic components. Contributions and novelties of our work can be identified as follows: 1) an HSI filtering framework is created, which can accommodate various Gabor filtering procedures and hence offer the potential to guide the design of new Gabor filters; 2) following such a unified filtering framework and taking into consideration both local spectral–spatial harmonic characteristics and range domain variations, we develop a new concept of spectral–spatial-range Gabor filtering; and 3) utilizing this proposed Gabor prototype and elaborating mathematical derivations, we achieve a novel discriminative spectral–spatial-range Gabor filtering method, which can deal with discriminative local harmonics and edge interference simultaneously along the spectral–spatial-range domain, obtaining highly discriminative Gabor features while yielding linear computational complexity. Our novel method is evaluated on four real HSI data sets and achieves excellent performances. [ABSTRACT FROM AUTHOR]

The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor-intensive and time-consuming. In this article, a multilabel training sample augmentation method is proposed. Instead of giving an exact label to each pixel, we just precisely label a small number of pixels by giving them a single label (called single-label samples) and annotate a large number of pixels in certain regions together by giving them multiple labels (called multilabel samples). Furthermore, in order to make full use of the multilabel training samples, a superpixel segmentation and recursive filtering-based method is proposed. The proposed method consists of the following major steps: recursive filtering-based feature extraction, superpixel-based segmentation, and spectral–spatial similarity-based mislabeled sample removal. Experimental results demonstrate that the proposed method can significantly improve the classification accuracy of multiple classifiers by using the multilabel training samples. [ABSTRACT FROM AUTHOR]

In this article, a novel game theory-based approach is proposed for anomaly detection in hyperspectral images (HSIs) via effectively exploring multiple spectral and spatial characteristics of anomalies. This approach comprises three main steps. First, spectral, extend morphological profiles (EMPs), and Gabor texture features are captured from an input HSI. Then, we define the anomaly detection problem as an anomaly game model, in which image regions (superpixels) of different features are modeled as players who select to be “anomaly” or “background” as their strategies. Three initial detection results are produced based on each player’s strategy in the Nash equilibrium of the anomaly game. Last, a saliency-based decision fusion technique is used to combine the complementary information in different features, so as to obtain a fused detection map. The performance of the proposed anomaly detection technique is evaluated on four real-scene HSIs. Experimental results validate that our approach can outperform some state-of-the-art anomaly detection methods. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) is an active research topic in the remote-sensing community. It aims at modeling mixed pixels using a collection of pure constituent materials (endmembers) weighted by their corresponding fractional abundances. Among existing unmixing schemes, nonnegative matrix factorization (NMF) has drawn significant attention due to its unsupervised nature, as well as its capacity to obtain both endmembers and fractional abundances simultaneously. In this article, we present a new blind unmixing method based on the generalized morphological component analysis (GMCA) framework, in which an additional constraint is introduced into the standard NMF model to represent the sparsity and morphological diversity of the abundance maps associated with each endmember. More specifically, we take into account the fact that different ground categories in a hyperspectral scene generally exhibit various spatial distributions and morphological characteristics. As a result, when providing a specific dictionary basis for these categories, their corresponding abundance maps (referred to as sources) can be sparsely represented. In addition, due to the low correlation between different sources, their sparse representations will not share the same most significant coefficients. With this observation in mind, we can further promote source discrimination and separation in the unmixing process. Moreover, in order to obtain a stable solution of the involved optimization problem, we adopt an alternate iterative constrained algorithm with a threshold descent strategy. Our experiments, carried out on both synthetic and real hyperspectral scenes, reveal that our newly developed GMCA-based unmixing method obtains very promising results with fast convergence speed and requiring significantly less parameter tuning. This confirms the advantage of the proposed spatial morphological component approach for HU purposes. [ABSTRACT FROM AUTHOR]

Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. [ABSTRACT FROM AUTHOR]

In this article, a novel hyperspectral anomaly detection method with kernel Isolation Forest (iForest) is proposed. The method is based on an assumption that anomalies rather than background can be more susceptible to isolation in the kernel space. Based on this idea, the proposed method detects anomalies as follows. First, the hyperspectral data are mapped into the kernel space, and the first $K$ principal components are used. Then, the isolation samples in the image are detected with the iForest constructed using randomly selected samples in the principal components. Finally, the initial anomaly detection map is iteratively refined with locally constructed iForest in connected regions with large areas. Experimental results on several real hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traditional hyperspectral imaging technique obtains numerous hyperspectral images (HSIs) with hundreds of spectral bands, leading to high cost in data acquisition, transmission, and storage. Compressed sensing (CS) theory provides a new imaging mechanism, which relies on the assumption that signals can be sparsely represented over a dictionary. By the CS imaging technique, original HSIs can be approximately reconstructed from only a few sampled measurements. In this article, a novel context-aware CS (CACS) method for HSIs is proposed by incorporating contextual prior to the dictionary learning and the sparse reconstruction. First, a patch-based online dictionary learning (ODL) algorithm is developed by introducing a joint sparse constraint. On the one hand, the online dictionary learning mechanism enables a more adaptive representation of HSIs with different scenes than using fixed-basis-based dictionaries, e.g., the discrete cosine transform (DCT) and the discrete wavelet transform dictionaries. On the other hand, the introduced joint sparse constraint promotes the learned dictionary to more sparsely and structurally represent spectral pixels. Then, with the well-learned dictionary, a weighted smoothing regularization is introduced to develop a new sparse reconstruction model. Considering the high spectral–spatial similarity of pixels in a neighborhood, the new sparse reconstruction model will encourage a locally smoothing reconstruction result. In this way, the spectral–spatial structures of the HSI can be well preserved, while possible artifacts can be effectively reduced. Experimental results demonstrate the superiority of the proposed method over some state-of-the-art hyperspectral compressive imaging methods. [ABSTRACT FROM AUTHOR]

In this article, a novel hyperspectral image (HSI) classification method based on fusing multiple edge-preserving operations (EPOs) is proposed, which consists of the following steps. First, the edge-preserving features are obtained by performing different types of EPOs, i.e., local edge-preserving filtering and global edge-preserving smoothing on the dimension-reduced HSI. Then, with the assistance of a superpixel segmentation method, the edge-preserving features are further improved by considering the inter and intra spectral properties of superpixels. Finally, the spectral and edge-preserving features are fused to form one composite kernel, which is fed into the support vector machine (SVM) followed by a majority voting fusion scheme. Experimental results on three data sets demonstrate the superiority of the proposed method over several state-of-the-art classification approaches, especially when the training sample size is limited. Furthermore, 21 well-known methods, including mathematical morphology-based approaches, sparse representation models, and deep learning-based classifiers, are adopted to be compared with the proposed method on Houston data set with standard sets of training and test samples released during 2013 Data Fusion Contest, which also shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Most of the existing hyperspectral anomaly detectors only consider the difference between anomaly pixels and background pixels. These methods may mistakenly detect some regions in a complex background that contains various ground covers, since some background regions and anomaly objects may have similar spectral or spatial characteristics. Therefore, with a new perspective, this paper introduces a novel manifold ranking-based detection framework (MRDF). In addition to capturing the difference between anomaly pixels and background pixels, this detection framework exploits the similarity between anomaly pixels for detection. Specifically, the proposed detection framework comprises three main steps. First, the Reed–Xiaoli method is applied to capture the spectral difference between anomaly pixels and background pixels, and an initial detection map can be obtained. A set of anomaly queries are obtained automatically by employing the binary segmentation to the initial detection map. Then, we construct a closed-loop graph to characterize the spatial similarity between adjoining nodes where each node is a superpixel. Finally, a manifold ranking technique is employed to estimate the ranking value of every node based on the similarity between the test node and anomaly queries. By normalizing the ranking value of each node, a final detection map is generated. Abundant experiments are conducted on four real hyperspectral data sets. It is found that the proposed detection framework obtains a better detection performance than the current state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral–spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments. [ABSTRACT FROM AUTHOR]

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features. [ABSTRACT FROM AUTHOR]

Mislabeled training samples may have a negative effect on the performance of hyperspectral image classification. In order to solve this problem, a new density peak (DP) clustering-based noisy label detection method is proposed, which consists of the following steps. First, the distances among the training samples of each class are calculated using four representative distance metrics, i.e., the Euclidean distance (ED), orthogonal projection divergence (OPD), spectral information divergence (SID), and correlation coefficient (CC). Then, the local density of each training sample can be obtained using the DP clustering algorithm. Finally, a local density-based decision function is used to detect the noisy labels. The effectiveness of the proposed method is evaluated using the support vector machines on several real hyperspectral data sets. Experimental results demonstrate that the proposed noisy label detection method indeed helps in improving the classification performance. [ABSTRACT FROM AUTHOR]

A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. [ABSTRACT FROM AUTHOR]

In the field of hyperspectral image classification, a widely used way for objective performance evaluation of different classification methods is calculating three accuracy indexes, i.e., the overall accuracy, the average accuracy, and the Kappa coefficient. These accuracy indexes are obtained by comparing the classification results with the ground truth, i.e., a reference classification map labeled by human experts. In this paper, the effect of ground truths on the objective performance evaluation of hyperspectral image classification is studied. The purpose is to investigate, if the ground truth is insufficient, whether the above accuracy indexes can be completely responsible. Furthermore, in order to measure the robustness of different classification methods to those insufficient ground truths, four evaluation metrics, i.e., the Pearson linear correlation coefficient, root-mean-square error, Spearman’s rank correlation coefficient, and Kendall’s rank correlation coefficient have been adopted for further analysis. Based on these experiments, an interesting conclusion can be obtained that insufficient ground truths may limit the assessment capability of existing accuracy indexes. This underlines that overoptimistic performance evaluations may exist and stresses the demand of designing more appropriate accuracy indexes for objective performance evaluation with insufficient ground truths. [ABSTRACT FROM AUTHOR]

This paper proposes a new method, called multilayer stacked covariance pooling (MSCP), for remote sensing scene classification. The innovative contribution of the proposed method is that it is able to naturally combine multilayer feature maps, obtained by pretrained convolutional neural network (CNN) models. Specifically, the proposed MSCP-based classification framework consists of the following three steps. First, a pretrained CNN model is used to extract multilayer feature maps. Then, the feature maps are stacked together, and a covariance matrix is calculated for the stacked features. Each entry of the resulting covariance matrix stands for the covariance of two different feature maps, which provides a natural and innovative way to exploit the complementary information provided by feature maps coming from different layers. Finally, the extracted covariance matrices are used as features for classification by a support vector machine. The experimental results, conducted on three challenging data sets, demonstrate that the proposed MSCP method can not only consistently outperform the corresponding single-layer model but also achieve better classification performance than other pretrained CNN-based scene classification methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to detect and correct mislabeled training samples for hyperspectral image classification. First, domain transform recursive filtering-based feature extraction is used to improve the separability of the training samples. Then, constrained energy minimization-based object detection is performed on the training set with each training sample serving as the object spectrum. Finally, the label of each training sample is verified or corrected based on the averaged detection probabilities of different classes. Experiments performed on real hyperspectral data sets demonstrate the effectiveness of the proposed method in improving classification performance with respect to the classifier trained with the original training set that contains a number of mislabeled samples. [ABSTRACT FROM AUTHOR]

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved good performance. In general, deep models adopt a large number of hierarchical layers to extract features. However, excessively increasing network depth will result in some negative effects (e.g., overfitting, gradient vanishing, and accuracy degrading) for conventional convolutional neural networks. In addition, the previous networks used in HSI classification do not consider the strong complementary yet correlated information among different hierarchical layers. To address the above two issues, a deep feature fusion network (DFFN) is proposed for HSI classification. On the one hand, the residual learning is introduced to optimize several convolutional layers as the identity mapping, which can ease the training of deep network and benefit from increasing depth. As a result, we can build a very deep network to extract more discriminative features of HSIs. On the other hand, the proposed DFFN model fuses the outputs of different hierarchical layers, which can further improve the classification accuracy. Experimental results on three real HSIs demonstrate that the proposed method outperforms other competitive classifiers. [ABSTRACT FROM AUTHOR]

In this paper, a novel local covariance matrix (CM) representation method is proposed to fully characterize the correlation among different spectral bands and the spatial–contextual information in the scene when conducting feature extraction (FE) from hyperspectral images (HSIs). Specifically, our method first projects the HSI into a subspace, using the maximum noise fraction method. Then, for each test pixel in the subspace, its most similar neighboring pixels (within a local spatial window) are clustered using the cosine distance measurement. The test pixel and its neighbors are used to calculate a local CM for FE purposes. Each nondiagonal entry in the matrix characterizes the correlation between different spectral bands. Finally, these matrices are used as spatial–spectral features and fed to a support vector machine for classification purposes. The proposed method offers a new strategy to characterize the spatial–spectral information in the HSI prior to classification. Experimental results have been conducted using three publicly available hyperspectral data sets for classification, indicating that the proposed method can outperform several state-of-the-art techniques, especially when the training samples available are limited. [ABSTRACT FROM AUTHOR]

A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy. [ABSTRACT FROM PUBLISHER]

Supervised classification of hyperspectral images (HSI) is a very challenging task due to the existence of noisy and mixed spectral characteristics. Recently, the widely developed spectral unmixing techniques offer the possibility to extract spectral mixture information at a subpixel level, which can contribute to the categorization of seriously mixed spectral pixels. Besides, it has been demonstrated that the discrimination between different materials will be improved by integrating the geometry and structure information, which can be derived from the variance between neighboring pixels. Furthermore, by incorporating the spatial context, the superpixel-based spectral–spatial similarity information can be used to smooth classification results in homogeneous regions. Therefore, a novel fusion framework for HSI classification that combines subpixel, pixel, and superpixel-based complementary information is proposed in this paper. Here, both feature fusion and decision fusion schemes are introduced. For the feature fusion scheme, the first step is to extract subpixel-level, pixel-level, and superpixel-level features from HSI, respectively. Then, the multiple feature-induced kernels are fused to form one composite kernel, which is incorporated with a support vector machine (SVM) classifier for label assignment. For the decision fusion scheme, class probabilities based on three different features are estimated by the probabilistic SVM classifier first. Then, the class probabilities are adaptively fused to form a probabilistic decision rule for classification. Experimental results tested on different real HSI images can demonstrate the effectiveness of the proposed fusion schemes in improving discrimination capability, when compared with the classification results relied on each individual feature. [ABSTRACT FROM PUBLISHER]

In the literature of remote sensing, deep models with multiple layers have demonstrated their potentials in learning the abstract and invariant features for better representation and classification of hyperspectral images. The usual supervised deep models, such as convolutional neural networks, need a large number of labeled training samples to learn their model parameters. However, the real-world hyperspectral image classification task provides only a limited number of training samples. This paper adopts another popular deep model, i.e., deep belief networks (DBNs), to deal with this problem. The DBNs allow unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. But the usual pretraining and fine-tuning method would make many hidden units in the learned DBNs tend to behave very similarly or perform as “dead” (never responding) or “potential over-tolerant” (always responding) latent factors. These results could negatively affect description ability and thus classification performance of DBNs. To further improve DBN’s performance, this paper develops a new diversified DBN through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework. The experiments over real-world hyperspectral images demonstrated that the diversity promoting prior in both pretraining and fine-tuning procedure lead to the learned DBNs with more diverse latent factors, which directly make the diversified DBNs obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods. [ABSTRACT FROM PUBLISHER]

Sparse representation (SR) can transform spectral signatures of hyperspectral pixels into sparse coefficients with very few nonzero entries, which can efficiently be used for compression. In this paper, a spectral–spatial adaptive SR (SSASR) method is proposed for hyperspectral image (HSI) compression by taking advantage of the spectral and spatial information of HSIs. First, we construct superpixels, i.e., homogeneous regions with adaptive sizes and shapes, to describe HSIs. Since homogeneous regions usually consist of similar pixels, pixels within each superpixel will be similar and share similar spectral signatures. Then, the spectral signatures of each superpixel can be simultaneously coded in the SR model to exploit their joint sparsity. Since different superpixels generally have different performances of SR, their rate–distortion performances in the sparse coding will be different. To achieve the best possible overall rate–distortion performance, an adaptive coding scheme is introduced to adaptively assign distortions to superpixels. Finally, the obtained sparse coefficients are quantized and entropy coded and constitute the final bitstream with the coded superpixel map. The experimental results over several HSIs show that the proposed SSASR method outperforms some state-of-the-art HSI compression methods in terms of the rate–distortion and spectral fidelity performances. [ABSTRACT FROM PUBLISHER]

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet-transformed domain via a relatively new spectral feature processing technique—singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracy over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artifacts introduced during the data acquisition process. By adding an extra spatial postprocessing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral–spatial classification methods. [ABSTRACT FROM PUBLISHER]

Active learning (AL) and semisupervised learning (SSL) are both promising solutions to hyperspectral image classification. Given a few initial labeled samples, this work combines AL and SSL in a novel manner, aiming to obtain more manually labeled and pseudolabeled samples and use them together with the initial labeled samples to improve the classification performance. First, based on a comparison of the segmentation and spectral–spatial classification results obtained by random walker (RW) and extended RW (ERW) algorithms, the unlabeled samples are separated into two different sets, i.e., low- and high-confidence unlabeled data sets. For the high-confidence unlabeled data, pseudolabeling is performed, which can ensure the correctness and informativeness of the pseudolabeled samples. For the low-confidence unlabeled data, AL is used to select samples. In this way, the samples which are more effective for improvement of classification performance can be labeled in only a few iterations. Finally, with the learned training set and the original hyperspectral image as inputs, the ERW classifier is used to obtain the final classification result. Experiments performed on three real hyperspectral data sets show that the proposed method can achieve competitive classification accuracy even with a very limited number of manually labeled samples. [ABSTRACT FROM PUBLISHER]

A novel hyperspectral image (HSI) classification method by the probabilistic fusion of pixel-level and superpixel-level classifiers is proposed. Generally, pixel-level classifiers based on spectral information only may generate “salt and pepper” result in the classification map since spatial correlation is not considered. By incorporating spatial information in homogeneous regions, the superpixel-level classifiers can effectively eliminate the noisy appearance. However, the classification accuracy will be deteriorated if undersegmentation cannot be fully avoided in superpixel-based approaches. Therefore, it is proposed to adaptively combine both the pixel-level and superpixel-level classifiers, to improve the classification performance in both homogenous and structural areas. In the proposed method, a support vector machine classifier is first applied to estimate the pixel-level class probabilities. Then, superpixel-level class probabilities are estimated based on a joint sparse representation. Finally, the two levels of class probabilities are adaptively combined in a maximum a posteriori estimation model, and the classification map is obtained by solving the maximum optimization problem. Experimental results on real HSI images demonstrate the superiority of the proposed method over several well-known classification approaches in terms of classification accuracy. [ABSTRACT FROM PUBLISHER]

A novel set-to-set distance-based spectral–spatial classification method for hyperspectral images (HSIs) is proposed. In HSIs, the spatially connected and spectrally similar pixels within each homogeneous region can be considered as one set of test samples, i.e., a test set, which should belong to the same class. In addition, each class of labeled pixels can be regarded as one set of training samples, i.e., a training set. Therefore, it is a natural consideration in the proposed method to measure the similarity between test and training sets via specific set-based distance criteria and then decide the classification label for each test set, accordingly. Specifically, the superpixel algorithm-based oversegmentation technique jointly exploits both the spatial similarity and structural information to first divide the HSI into multiple perceptually uniform regions. As a result, each segmented region corresponds to one test set. Then, each test/training set is represented with an affine hull (AH) model, which utilizes both the similarity and variance of pixels within each set to adaptively characterize the set. Finally, the class label for each test set is determined based on the closest geometry distance between test and training AHs. Experimental results on real HSI data sets demonstrate the superiority of the proposed algorithm over several well-known classification approaches, in terms of classification accuracy and computational speed. [ABSTRACT FROM PUBLISHER]

In this paper, a novel spectral–spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral–spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral–spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral–spatial information of superpixels via multiple kernels, which is termed as superpixel-based classification via multiple kernels (SC-MK). In the HSI, each superpixel can be regarded as a shape-adaptive region, which consists of a number of spatial neighboring pixels with very similar spectral characteristics. First, the proposed SC-MK method adopts an oversegmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information, as well as spatial information, within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machine classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods. [ABSTRACT FROM PUBLISHER]

Pansharpening aims to synthesize a high-spatial-resolution multispectral (MS) image by fusing a panchromatic (PAN) image and a low-resolution MS image. The multiresolution analysis (MRA)-based methods are a popular group of pansharpening methods. However, in the MRA-based methods, spatial distortions may occur in the pansharpened product due to the misalignment of PAN and MS data. To address the spatial distortion issue in MRA-based methods, this paper proposes a two-step approach, which consists of the coarse step and the refined step. The coarse step produces a preliminary result using the traditional details injection model. Then, the preliminary product is refined with a second details injection operation in the refined step. Moreover, in our proposed two-step approach, a novel multiscale decomposition based on a normalized nonlocal means (NNLM) filter is developed to extract the spatial detail. Compared with the original nonlocal means filter, the designed NNLM makes the similarity measure more robust and accurate by exploiting the normalized intensity value and the mean value jointly. The experimental results on various satellite data demonstrate the superiority of the proposed pansharpening scheme by comparing with ten well-known methods. [ABSTRACT FROM PUBLISHER]

Remote sensing image fusion can integrate the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image to produce a fused MS image with high spatial resolution. In this paper, a remote sensing image fusion method is proposed with sparse representations over learned dictionaries. The dictionaries for PAN image and low-resolution MS image are learned from the source images adaptively. Furthermore, a novel strategy is designed to construct the dictionary for unknown high-resolution MS images without training set, which can make our proposed method more practical. The sparse coefficients of the PAN image and low-resolution MS image are sought by the orthogonal matching pursuit algorithm. Then, the fused high-resolution MS image is calculated by combining the obtained sparse coefficients and the dictionary for the high-resolution MS image. By comparing with six well-known methods in terms of several universal quality evaluation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

This paper addresses the remote sensing image pan-sharpening problem from the perspective of compressed sensing (CS) theory which ensures that with the sparsity regularization, a compressible signal can be correctly recovered from the global linear sampled data. First, the degradation model from a high- to low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image is constructed as a linear sampling process which is formulated as a matrix. Then, the model matrix is considered as the measurement matrix in CS, so pan-sharpening is converted into signal restoration problem with sparsity regularization. Finally, the basis pursuit (BP) algorithm is used to resolve the restoration problem, which can recover the high-resolution MS image effectively. The QuickBird and IKONOS satellite images are used to test the proposed method. The experimental results show that the proposed method can well preserve spectral and spatial details of the source images. The pan-sharpened high-resolution MS image by the proposed method is competitive or even superior to those images fused by other well-known methods. [ABSTRACT FROM AUTHOR]