Object detection in remote sensing scenarios plays an indispensable and significant role in civilian, commercial, and military areas, leveraging the power of convolutional neural networks (CNNs). Remote sensing images, captured by crafts and satellites, exhibit unique characteristics including complicated backgrounds, limited features, distinct density, and varied scales. The contextual and comprehensive information in an image can make a detector precisely localize and classify targets, which is extremely valuable for object detection in remote sensing scenarios. However, CNNs, restricted by the essence of the convolution operation, possess local receptive fields and scarce contextual information, even in large models. To address this limitation and improve detection performance by extracting global contextual information, we propose a novel plug-and-play attention module, named Convolution with Transformer Attention Module (CTAM). CTAM is composed of a convolutional bottleneck block and a simplified Transformer layer, which can facilitate the integration of local features and position information with long-range dependency. YOLOv8n, a superior and faster variant of the YOLO series, is selected as the baseline. To demonstrate the effectiveness and efficiency of CTAM, we incorporated CTAM into YOLOv8n and conducted extensive experiments on the DIOR dataset. YOLOv8n-CTAM achieves an impressive 54.2 mAP@50-95, surpassing YOLOv8n (51.4) by a large margin. Notably, it outperforms the baseline by 2.7 mAP@70 and 4.4 mAP@90, showcasing its superiority with stricter IoU thresholds. Furthermore, the experiments conducted on the TGRS-HRRSD dataset validate the excellent generalization ability of CTAM.

Hyperspectral unmixing (HU) aims to decompose mixed pixels into a set of endmembers and corresponding abundances. Deep learning-based HU methods are currently a hot research topic, but most existing unmixing methods still rely on per-pixel training or employ convolutional neural networks (CNNs), which overlook the non-local correlations of materials and spectral characteristics. Furthermore, current research mainly focuses on linear mixing models, which limits the feature extraction capability of deep encoders and further improvement in unmixing accuracy. In this paper, we propose a nonlinear unmixing network capable of extracting global spatial-spectral features. The network is designed based on an autoencoder architecture, where a dual-stream CNNs is employed in the encoder to separately extract spectral and local spatial information. The extracted features are then fused together to form a more complete representation of the input data. Subsequently, a linear projection-based multi-head self-attention mechanism is applied to capture global contextual information, allowing for comprehensive spatial information extraction while maintaining lightweight computation. To achieve better reconstruction performance, a model-free nonlinear mixing approach is adopted to enhance the model’s universality, with the mixing model learned entirely from the data. Additionally, an initialization method based on endmember bundles is utilized to reduce interference from outliers and noise. Comparative results on real datasets against several state-of-the-art unmixing methods demonstrate the superior of the proposed approach.

Object detection in remote sensing is becoming a conspicuous challenge with the rapidly increasing quantity and quality of remote sensing images. Although the application of Deep Learning has obtained remarkable performance in Computer Vision, detecting multi-scale targets in remote sensing images is still an unsolved problem, especially for small instances which possess limited features and intricate backgrounds. In this work, we managed to cope with this problem by designing a neck attention block (NAB), a simple and flexible module which combines the convolutional bottleneck structure and the attention mechanism, different from traditional attention mechanisms that focus on designing complicated attention branches. In addition, Vehicle in High-Resolution Aerial Imagery (VHRAI), a diverse, dense, and challenging dataset, was proposed for studying small object detection. To validate the effectiveness and generalization of NAB, we conducted experiments on a variety of datasets with the improved YOLOv3, YOLOv4-Tiny, and SSD. On VHRAI, the improved YOLOv3 and YOLOv4-Tiny surpassed the original models by 1.98% and 1.89% mAP, respectively. Similarly, they exceeded the original models by 1.12% and 3.72% mAP on TGRS-HRRSD, a large multi-scale dataset. Including SSD, these three models also showed excellent generalizability on PASCAL VOC.

Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. Spatial analysis and dynamic degree were used to analyze the evolutionary process and spatiotemporal association of land cover from 1990 to 2020; the transfer matrix was used to analyze and reveal dynamic conversions of land cover from 1990 to 2000, 2000 to 2010, and 2010 to 2020; and the GM (1,1) model was used to forecast the changes in land cover by 2025 based on historical data. The results indicated that the dominance of forest and cropland did not change from 1990 to 2020, and the average area of forest and cropland was 512,713 km² and 486,322 km², respectively. The mutual conversion between cropland, forest, grassland, and bare areas was the most frequent. The area of cropland converted into forest and grassland was 14,167 km² and 25,217 km², respectively, and the area of forest and grassland converted into cropland was 27,682 km² and 23,764 km², respectively, from 1990 to 2000. A similar law of land cover change was also presented from 2000 to 2020. In addition, the predicted values of cropland, forest, grassland, shrubland, wetland, water bodies, impervious surfaces, and bare areas were 466,942 km², 499,950 km², 231,524 km², 1329 km², 11,775 km², 18,453 km², 30,549 km², and 189,973 km², respectively, by 2025. The maximum and minimum residuals between the predicted and actual values were 6241 km² and −156 km² from 1990 to 2020. The evaluation results of the GM (1,1) model showed that all of the evaluation indices were within an acceptable range, and that the posteriori error ratio and class ratio dispersion were both less than 0.25. Through comparison with other studies, this study is not only able to provide some experience for further analyzing the spatial and temporal changes in land cover and its future prediction but also provide a basis for comprehensive management in Northeast China.

Given the problem of spatial detail loss and spectral feature degradation in hyperspectral images (HSIs) characterized as blur, often caused by noise during image acquisition, and methods of removing blur noise designed on HSIs being insufficient, we propose an HSI reconstruction network based on a Blur–Kernel–Prior (BKP) method and Spectral–Spatial Attention (SSA) strategy for noise removal and reconstruction of HSIs. Specifically, a grouping strategy is designed to segment the HSIs into spectral dimension sub-images, and the BKP module, based on U-Net, learns the spatially adaptive blur kernel to extract and remove blurred features from each sub-image while preserving spatial features with spatial resolution. Subsequently, the SSA block is employed to extract shallow features, details, and edge information using a hybrid 2D–3D convolution from the sub-images, followed by deep feature extraction using a deep ResNet and multi-head attention (MSA) on the merged image to maximize the preservation of spectral dimension information. The $L_1$ loss function, combined with spectral dimension loss and peak signal-to-noise ratio loss, is utilized to constrain and ensure reconstruction accuracy. Experiments on both synthetic and real datasets demonstrate that our method exhibits excellent performance in reconstructing HSIs affected by blurred noise, outperforming existing methods in terms of quantitative quality and recovery of spectral dimension information.

Anomaly detection is hampered by band redundancy and the restricted reconstruction ability of spectral–spatial information in hyperspectral remote sensing. A novel hyperspectral anomaly detection method integrating differential attribute profiles and genetic algorithms (DAPGA) is proposed to sufficiently extract the spectral–spatial features and automatically optimize the selection of the optimal features. First, a band selection method with cross-subspace combination is employed to decrease the spectral dimension and choose representative bands with rich information and weak correlation. Then, the differentials of attribute profiles are calculated by four attribute types and various filter parameters for multi-scale and multi-type spectral–spatial feature decomposition. Finally, the ideal discriminative characteristics are reserved and incorporated with genetic algorithms to cluster each differential attribute profile by dissimilarity assessment. Experiments run on a variety of genuine hyperspectral datasets including airport, beach, urban, and park scenes show that the effectiveness of the proposed algorithm has great improvement with existing state-of-the-art algorithms.