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Hyperspectral image change detection (HSI-CD) is a task that utilizes both spectral and spatial features to more effectively detect changes. The features of HSI captured at different times are often influenced by external factors. The annoying variability is not useful for CD. Current methods extract effective information through complex learning together with it. They did not focus on whether different categories (changed and unchanged) of sample pairs have the same information composition. They lack a fundamental analysis and optimization based on the composition of information pairs to address current issues such as insufficient recognition of changed samples and poor feature fusion. To address these issues, we rethink the information composition of bitemporal sample pairs in different categories for HSI-CD, and design a frequency-domain information exchange and generation network with Siamese U-shaped structure (FDIEG-UNet) for HSI-CD. Our contribution can be summarized as follows: 1) by designing the FDGJLS module for learning and separating global-joint spectral-spatial features, to provide better basic frequency-domain information for subsequent feature-domain optimization; 2) based on the information from FDGJLS, we use the ULDT and SSTDFE modules to remove the influence of temporal-correlated invalid style information in feature fusion. Two modules work together to improve the equality of feature description between changed and unchanged samples in CD; and 3) the experimental results on three real HSI-CD datasets demonstrate the effectiveness of our proposed method in solving the above problems. The code is available at https://github.com/WUTCM-Lab/FDIEG-UNet.

Recently, oriented object detection in remote sensing images has garnered significant attention due to its broad range of applications. Early-oriented object detection adhered to the established general object detection frameworks, utilizing the label assignment strategy based on the horizontal bounding box (HBB) annotations or rotation-agnostic cost function. Such a strategy may not reflect the large aspect ratio and rotation of arbitrary-oriented objects in remote sensing images and require high parameter-tuning efforts in the training process, which will eventually harm the detector performance. Furthermore, the localization quality of oriented objects depends on precise rotation angle prediction, exacerbating the inconsistency between classification and regression tasks in oriented object detection. To address these issues, we propose the Gaussian distribution cost optimal transport assignment (GCOTA) and decoupled layer attention angle head (DLAAH). Specifically, GCOTA utilizes a Gaussian distribution-based cost function for the optimal transport (OT) label assignment in the training process, alleviating the impact of rotation angle and large aspect ratio in remote sensing images. DLAAH predicts rotation angle independently and incorporates layer attention to obtain the task-specific features based on the shared FPN features, enhancing the angle prediction and improving consistency across different tasks. Based on these proposed components, we present an anchor-free oriented detector, namely, Gaussian distribution and task-decoupled head oriented detector (GTDet) and a multiclass ship detection dataset in real scenarios (CGWX), which provides a benchmark for fine-grained object recognition in remote sensing images. Comprehensive experiments are conducted on CGWX and several public challenging datasets, including DOTAv1.0, and HRSC2016, to demonstrate that our method achieves superior performance on oriented object detection tasks. The code is available at https://github.com/WUTCM-Lab/GTDet.

Remote-sensing (RS) scene classification aims to classify RS images with similar scene characteristics into one category. Plenty of RS images are complex in background, rich in content, and multiscale in target, exhibiting the characteristics of both intraclass separation and interclass convergence. Therefore, discriminative feature representations designed to highlight the differences between classes are the key to RS scene classification. Existing methods represent scene images by extracting either global context or discriminative part features from RS images. However, global-based methods often lack salient details in similar RS scenes, while part-based methods tend to ignore the relationships between local ground objects, thus weakening the discriminative feature representation. In this article, we propose to combine global context and part-level discriminative features within a unified framework called CGINet for accurate RS scene classification. To be specific, we develop a light context-aware attention block (LCAB) to explicitly model the global context to obtain larger receptive fields and contextual information. A co-enhanced loss module (CELM) is also devised to encourage the model to actively locate discriminative parts for feature enhancement. In particular, CELM is only used during training and not activated during inference, which introduces less computational cost. Benefiting from LCAB and CELM, our proposed CGINet improves the discriminability of features, thereby improving classification performance. Comprehensive experiments over four benchmark datasets show that the proposed method achieves consistent performance gains over state-of-the-art (SOTA) RS scene classification methods.

In recent years, with the continuous advancement of remote sensing (RS) technology and text processing techniques, there has been a growing abundance of RS images and associated textual data. Combining RS images with their corresponding textual data allows for integrated analysis and retrieval, which holds significant practical implications across multiple application domains, including geographic information systems (GIS), environmental monitoring, and agricultural management. RS images have the characteristics of multitargets and multiscales, and the textual descriptions of these targets are not fully utilized, leading to a decrease in retrieval accuracy. Previous methods have struggled to balance intermodality information interaction and intramodality feature fusion, and they have paid little attention to the consistency of distribution within modalities. In light of this, this article proposes a symmetric multilevel guidance network (SMLGN) for cross-modal retrieval in RS. SMLGN first introduces fusion guidance between local and global within modalities and fine-grained bidirectional guidance between modalities, allowing for the learning of a common semantic space. Furthermore, to address the distribution differences of different modalities within the common semantic space, we design an adversarial joint learning framework and a multiobjective loss function to optimize the SMLGN method and achieve consistency in data distribution. The experimental results demonstrate that the SMLGN method performs well in the task of cross-modal retrieval between RS images and textual data. It effectively integrates the information from both modalities, improving the accuracy and reliability of the retrieval process.

Unlike conventional hyperspectral image (HSI) classification in general scenes, agricultural HSI classification poses greater challenges due to the increased occurrence of “same spectrum different object” and “different spectrum same object” phenomena caused by class similarities. Furthermore, the dense spatial distribution of land cover categories in agricultural scenes and the mixing of spatial–spectral features at crop boundaries add to the complexity of agricultural HSIs. To tackle these issues, we propose SANet, a network designed to enhance crop classification. SANet integrates spectral and contextual information while emphasizing self-correlation within the HSIs. It combines the spatial–spectral nonlocal block structure and the multiscale spectral self-attention (SSA) structure, allocating more attention resources to spatial and spectral dimensions and modeling the existing correlations within the spectral–spatial domain. Additionally, we introduce a two-branch spatial–spectral semantic extraction and fusion structure that can adaptively learn results from both branches. Experimental results demonstrate the promising performance of SANet in agricultural HSI classification by effectively utilizing spectral data, contextual information, and self-attention mechanisms.

Hyperspectral image change detection (HSI-CD) is a fundamental task in the field of remote sensing (RS) observation, which utilizes the rich spectral and spatial information in bitemporal HSIs to detect subtle changes on the Earth’s surface. However, modern deep learning (DL)-based HSI-CD methods mostly rely on patch-based methods, which leads to spectral band redundancy and spatial information noise in limited receiving domains, thus ignoring the extraction and utilization of saliency information and limiting the improvement of CD performance. To address these issues, this article proposes a joint saliency temporal–spatial–spectral information network (STSS-Net) for HSI-CD. The principal contributions of this article can be summarized: 1) we have designed a spatial saliency information extraction (SSIE) module for denoising based on distance from center pixels and spectral similarity of the substance, which increases the attention to spatial differences between similar spectral substances and different spectral substances; 2) we have designed a compact high-level spectral information tokenizer (CHLSIT) for spectral saliency information, where the high-level conceptual information of changes in spectral interest can be represented by nonlinear combinations of spectral bands, and redundancy can be removed by extracting high-level spectral conceptual features; and 3) utilizing the advantages of CNN and transformer architectures to combine temporal–spatial–spectral information. The experimental results on three real HSI-CD datasets show that STSS-Net can improve the accuracy of CD and has a certain improvement in the detection of edge information and complex information.

An important challenge that existing work has yet to address is the relatively small differences in audio representations compared with the rich content provided by remote sensing (RS) images, making it easy to overlook certain details in the images. This imbalance in information between modalities poses a challenge in maintaining consistent representations. In response to this challenge, we propose a novel cross-modal RS image-audio (RSIA) retrieval method called adaptive learning for aligning correlation (ALAC). ALAC integrates region-level learning into image annotation through a region-enhanced learning attention (RELA) module. By collaboratively suppressing features at different region levels, ALAC is able to provide a more comprehensive visual feature representation. In addition, a novel adaptive knowledge transfer (AKT) strategy has been proposed, which guides the learning process of the frontend network using aligned feature vectors. This approach allows the model to adaptively acquire alignment information during the learning process, thereby facilitating better alignment between the two modalities. Finally, to better use mutual information between different modalities, we introduce a plug-and-play result rerank module. This module optimizes the similarity matrix using retrieval mutual information between modalities as weights, significantly improving retrieval accuracy. Experimental results on four RSIA datasets demonstrate that ALAC outperforms other methods in retrieval performance. Compared with state-of-the-art methods, improvements of 1.49%, 2.25%, 4.24%, and 1.33% were, respectively, achieved by ALAC. The codes are accessible at https://github.com/huangjh98/ALAC.

Semantic segmentation of ultrahigh-resolution (UHR) remote sensing images is a fundamental task for many downstream applications. Achieving precise pixel-level classification is paramount for obtaining exceptional segmentation results. This challenge becomes even more complex due to the need to address intricate segmentation boundaries and accurately delineate small objects within the remote sensing imagery. To meet these demands effectively, it is critical to integrate two crucial components: global contextual information and spatial detail feature information. In response to this imperative, the multilevel context-aware segmentation network (MCSNet) emerges as a promising solution. MCSNet is engineered to not only model the overarching global context but also extract intricate spatial detail features, thereby optimizing segmentation outcomes. The strength of MCSNet lies in its two pivotal modules, the spatial detail feature extraction (SDFE) module and the refined multiscale feature fusion (RMFF) module. Moreover, to further harness the potential of MCSNet, a multitask learning approach is employed. This approach integrates boundary detection and semantic segmentation, ensuring that the network is well-rounded in its segmentation capabilities. The efficacy of MCSNet is rigorously demonstrated through comprehensive experiments conducted on two established international society for photogrammetry and remote sensing (ISPRS) 2-D semantic labeling datasets: Potsdam and Vaihingen. These experiments unequivocally establish MCSNet stands as a pioneering solution, that delivers state-of-the-art performance, as evidenced by its outstanding mean intersection over union (mIoU) and mean $F1$ -score (mF1) metrics. The code is available at: https://github.com/WUTCM-Lab/MCSNet.

Aerial scene classification, aiming at assigning a specific semantic class to each aerial image, is a fundamental task in the remote sensing community. Aerial scene images have more diverse and complex geological features. While some statistics of images can be well fit using convolution, it limits such models to capturing the global context hidden in aerial scenes. Furthermore, to optimize the feature space, many methods add class information to the feature embedding space. However, they seldom combine model structure with class information to obtain more separable feature representations. In this article, we propose to address these limitations in a unified framework (i.e., CGFNet) from two aspects: focusing on the key information of input images and optimizing the feature space. Specifically, we propose a global-group attention module (GGAM) to adaptively learn and selectively focus on important information from input images. GGAM consists of two parallel branches: the adaptive global attention branch (AGAB) and the region-aware attention branch (RAAB). AGAB utilizes an adaptive pooling operation to better model the global context in aerial scenes. As a supplement to AGAB, RAAB combines grouping features with spatial attention to spatially enhance the semantic distribution of features (i.e., selectively focus on effective regions of features and ignore irrelevant semantic regions). In parallel, a focal attention loss (FA-Loss) is exploited to introduce class information into attention vector space, which can improve intraclass consistency and interclass separability. Experimental results on four publicly available and challenging datasets demonstrate the effectiveness of our method. The source code will be released at: https://github.com/zoecheno/CGFNet.

Remote-sensing image–text (RSIT) retrieval involves the use of either textual descriptions or remote-sensing images (RSI) as queries to retrieve relevant RSIs or corresponding text descriptions. Many traditional cross-modal RSIT retrieval methods tend to overlook the importance of capturing salient information and establishing the prior similarity between RSIs and texts, leading to a decline in cross-modal retrieval performance. In this article, we address these challenges by introducing a novel approach known as multiscale salient image-guided text alignment (MSITA). This approach is designed to learn salient information by aligning text with images for effective cross-modal RSIT retrieval. The MSITA approach first incorporates a multiscale fusion module and a salient learning module to facilitate the extraction of salient information. In addition, it introduces an image-guided text alignment (IGTA) mechanism that uses image information to guide the alignment of texts, enabling the effective capture of fine-grained correspondences between RSI regions and textual descriptions. In addition to these components, a novel loss function is devised to enhance the similarity across different modalities and reinforce the prior similarity between RSIs and texts. Extensive experiments conducted on four widely adopted RSIT datasets affirm that the MSITA approach significantly enhances cross-modal RSIT retrieval performance in comparison to other state-of-the-art methods.

Cross-modal remote sensing image–audio (RSIA) retrieval aims to use audio or remote sensing images (RSIs) as queries to retrieve relevant RSIs or corresponding audios. Although many approaches leverage labeled samples to achieve good performance, the performance cost of labeled samples is high, because cross-modal remote sensing (RS) labeled samples usually require huge labor resources. Therefore, unsupervised cross-modal learning is very important in real-world applications. In this article, we propose a novel unsupervised cross-modal RSIA retrieval approach, named self-supervision interactive alignment (SSIA), which can take advantage of large amounts of unlabeled samples to learn the salient information, cross-modal alignment, and the similarity between RSIs and audios. Since self-supervised learning lacks the supervision of label information, we leverage the similarity between the input RSI information and audio information as the supervision information. Besides, to perform cross-modal alignment, a novel interactive alignment (IA) module is designed to explore fine correspondence relation for RSIs and audios. Moreover, we design an audio-guided image de-redundant module to reduce the redundant information of visual information, which can capture salient information of RSIs. Extensive experiments on four widely used RSIA datasets testify that the SSIA performance gains better RSIA retrieval performance than other compared approaches.

For cross-modal remote sensing image-audio (RSIA) retrieval task, hashing technology has attracted much attention in recent works. Most of them focus on mapping RS images and audios into a Hamming space, whilst neglecting discriminative information of RS images and fine alignment for RS images and audios. In this article, we tackle these dilemmas with a novel fine aligned discriminative hashing (FADH) approach, which can learn hash codes to capture discriminative information of RS images and learn the corresponding detailed information between RS images and audios simultaneously. We first develop a new discriminative information learning module to learn discriminative information about RS images. Meanwhile, a fine alignment module is proposed to unearth the fine correspondence for RS image regions and audios, which can effectively improve the retrieval performance. On top of the two paths, we design a new objective function, which can maintain the similarity of hash codes, preserve the semantic information of RS image features and audio features and eliminate cross-modal differences. The reliability and significance of the designed framework are effectively demonstrated by diverse experiments on three RSIA datasets.

Deep hashing algorithms are widely exploited in retrieval tasks due to their low storage and retrieval efficiency. Most of them focus on global feature learning, while neglecting local fine-grained features and saliency information for drone images. In this article, we tackle these dilemmas with a novel deep saliency smoothing hashing (DSSH) algorithm, which can leverage saliency capture mechanism, distribution smoothing term, global features, and local fine-grained features to learn effective hash codes for drone image retrieval. The DSSH algorithm first designs an information extraction module to capture global features and local fine-grained features for drone images. Meanwhile, a saliency capture module is proposed to perform information interaction attention and visual enhancement attention, which can capture the saliency area of drone images effectively. On top of the two paths, a novel objective function is designed to preserve the similarity of hash codes, smooth the distribution of drone image datasets, and reduce the quantization errors between hash codes and hash-like codes concurrently. Extensive experiments on the Drone Action Dataset and ERA Drone Dataset demonstrate that the DSSH algorithm can further improve the retrieval performance compared with other deep hashing algorithms.

Hyperspectral image (HSI) has rich spatial–spectral information, high spectral correlation, and large redundancy between information. Due to the sparse background distribution of HSI, the existing methods generally perform poorly for the classification of class pixels located in the boundary areas of land cover categories. This is largely because the network is vulnerable to surrounding redundant information during the training stage, leading to inaccurate feature extraction and thus poor generalization ability of the model. Based on previous work, we propose an HSI classification network called MATNet which combines multi-attention and transformer. The network first uses spatial attention (SA) and channel attention (CA) to pay more attention to the more significant information parts, then uses the tokenizer module to make a semantic-level representation of different categories of ground objects, and then performs deep semantic feature extraction using the transformer encoder module. Finally, we design a loss function called Lpoly, which adds a polynomial to the label smoothing loss to tune the original first polynomial to accommodate different datasets and tasks. We perform experiments in several well-known HSI datasets as well as for visualization. The results show that our proposed MATNet performs well in extracting spatial–spectral features of HSIs and understanding semantic degrees of semantic degrees.

For large-scale image retrieval, hashing has been extensively explored in approximate nearest neighbor search methods due to its low storage and high computational efficiency. With the development of deep learning, deep hashing methods have made great progress in image retrieval. Most existing deep hashing methods cannot fully consider the intra-group correlation of hash codes, which leads to the correlation decrease problem of similar hash codes and ultimately affects the retrieval results. In this article, we propose an end-to-end siamese dilated inception hashing (SDIH) method that takes full advantage of multi-scale contextual information and category-level semantics to enhance the intra-group correlation of hash codes for hash codes learning. First, a novel siamese inception dilated network architecture is presented to generate hash codes with the intra-group correlation enhancement by exploiting multi-scale contextual information and category-level semantics simultaneously. Second, we propose a new regularized term, which can force the continuous values to approximate discrete values in hash codes learning and eventually reduces the discrepancy between the Hamming distance and the Euclidean distance. Finally, experimental results in five public data sets demonstrate that SDIH can outperform other state-of-the-art hashing algorithms.

For large-scale image retrieval task, a hashing technique has attracted extensive attention due to its efficient computing and applying. By using the hashing technique in image retrieval, it is crucial to generate discrete hash codes and preserve the neighborhood ranking information simultaneously. However, both related steps are treated independently in most of the existing deep hashing methods, which lead to the loss of key category-level information in the discretization process and the decrease in discriminative ranking relationship. In order to generate discrete hash codes with notable discriminative information, we integrate the discretization process and the ranking process into one architecture. Motivated by this idea, a novel ranking optimization discrete hashing (RODH) method is proposed, which directly generates discrete hash codes (e.g., +1/−1) from raw images by balancing the effective category-level information of discretization and the discrimination of ranking information. The proposed method integrates convolutional neural network, discrete hash function learning, and ranking function optimizing into a unified framework. Meanwhile, a novel loss function based on label information and mean average precision (MAP) is proposed to preserve the label consistency and optimize the ranking information of hash codes simultaneously. Experimental results on four benchmark data sets demonstrate that RODH can achieve superior performance over the state-of-the-art hashing methods.

A pair of enantiomeric polyketides, (+)- and (−)-alternamgin (1), featuring an unprecedented 6/6/6/6/5/6/6 seven ring backbone, were isolated from the endophytic fungi Alternariasp. MG1. The relative configuration of 1was determined using X-ray diffraction, and the absolute configurations of (±)-1were confirmed by comparing the experimental and calculated ECD data. Plausible biosynthetic pathways for 1were proposed. Compound (−)-1exhibited moderate necrosis rates to Hela and HepG2 cells, but (+)-1only showed similar necrosis rates to HepG2 cells.

Because of the special characteristics of aero-engine precision-forging blade edge, the edge profile shape errors are easily caused by using conventional processing methods. The purpose of this research is to improve real-R profile shape errors and also exploring the belt grinding mechanism for the high-strength materials. Firstly, the grinding characteristics and process planning to the blade real-R edge are analysed. And then, the methodology of EBG with CBN belt is illustrated, at the same time, the control equation for EBG is established. Finally, the method of EBG with CBN belt is analysed by the experiment investigated to the aero-engine precision-forging blade, comparing to the method of hand. The experimental results show that the real-R profile shape errors ≤ ± 0.07 mm, and the real-R shape smoothing, profile clear and the inter sectional surface smooth transition. It is verified that the method of EBG is better than the conventional method.

In order to improve dimensional accuracy of aero-engine blade edges, surface removal contour (SRC) model is applied to the engine blade grinding in this study. Firstly, according to variable curvature of characteristics the engine blades, this paper adopts the semi-Hertz contact theory to simulate grinding contact state. Secondly, surface removal contour model was deduced from the material removal rate (MRR) nonlinear model, and the model of the final grinding depth is proposed, and the model consider the influence of path interval. Thirdly, for determining the parameters of MRR nonlinear and linear model, abrasive belt grinding experiment is carried out, which shows the relative error of MRR nonlinear model to be -1.1↓~1.4↓. The application of abrasive belt grinding on the blade showed the maximum error of the processing is within 0.05 mm and the abrasive belt grinding process system is stable.

In vitro 3D growth of tumors is a new cell culture model that more closely mimics the features of the in vivo environment and is being used increasingly in the field of biological and medical research. It has been demonstrated that cancer cells cultured in 3D matrices are more radio-resistant compared with cells in monolayers. However, the mechanisms causing this difference remain unclear. Here we show that cancer cells cultured in a 3D microenvironment demonstrated an increase in cells with stem cell properties. This was confirmed by the finding that cells in 3D cultures upregulated the gene and protein expression of the stem cell reprogramming factors such as OCT4, SOX2, NANOG, LIN28 and miR-302a, compared with cells in monolayers. Moreover, the expression of β-catenin, a regulating molecule of reprogramming factors, also increased in 3D-grown cancer cells. These findings suggest that cancer cells were reprogrammed to become stem cell–like cancer cells in a 3D growth culture microenvironment. Since cancer stem cell–like cells demonstrate an increased radio-resistance and chemo-resistance, our results offer a new perspective as to why. Our findings shed new light on understanding the features of the 3D growth cell model and its application in basic research into clinical radiotherapy and medicine.