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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.

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.

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.