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Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. [ABSTRACT FROM AUTHOR]

Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. [ABSTRACT FROM AUTHOR]

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches. [ABSTRACT FROM AUTHOR]

The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66). [ABSTRACT FROM AUTHOR]

In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral–spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral–spatial fusion gating mechanism (ASSF), which captures spectral–spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral–spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively. [ABSTRACT FROM AUTHOR]