Change detection identifies differences between images captured at different times. Real-world change detection faces challenges posed by the diverse and intricate nature of change areas, while current datasets and algorithms are often limited to simpler, consistent changes, reducing their effectiveness in practical applications. Existing dual-branch methods process images independently, risking the loss of change information due to insufficient early interaction. In contrast, single-stream approaches, though improving early integration, lack efficacy in capturing complex changes. To address these limitations, we introduce a novel single-stream framework, the Multi-scale Change-Aware Transformer (MCAT), which features the Dynamic Change-Aware Attention module and the Multi-scale Change-Enhanced Aggregator. The Dynamic Change-Aware Attention module, integrating local self-attention and cross-temporal attention, conducts dynamic iteration on images differences, thereby targeting feature extraction of change areas. The Multi-scale Change-Enhanced Aggregator enables the model to adapt to various scales and complex shapes through local change enhancement and multi-scale aggregation strategies. To overcome the limitations of existing datasets regarding the scale diversity and morphological complexity of change areas, we construct the Mining Area Change Detection dataset. The dataset offers a diverse array of change areas that span multiple scales and exhibit complex shapes, providing a robust benchmark for change detection. Extensive experiments demonstrate that our model outperforms existing methods, especially for irregular and multi-scale changes. Codes and dataset are available at https://github.com/chh11/MCAT.

Fine-grained segmentation of remote sensing mineral images plays a crucial role in the investigation and monitoring of mineral resource. In deep learning methods, fine-grained segmentation and edge detection are closely related in both data construction and feature extraction. However, the open-pit mineral areas in remote sensing images are heavily affected by complex natural environmental interference, posing challenges for precise data annotation and dataset construction. In view of this, we introduce the Fine-RSMI dataset, which includes a total of 10,225 images with finely annotated edges, while also introducing challenges such as multi-scale and edge irregularities. To tackle the challenge of fine-grained segmentation in irregular edges, we propose a hierarchical fusion edge feature enhancement framework. Our framework consists of an Edge Detail Feature Enhancement Module (EDFEM) and an Edge Supervision Module (ESM). EDFEM vertically cascades multiple feature fusion units to obtain high-order complementary information for refining edge features. ESM further supervises network reinforcement learning of mineral area edges using ground truth edge maps to improve edge segmentation performance. Both modules work in a plug-and-play manner, enabling effortless integration into existing segmentation networks. Our method achieves further performance improvement in many general remote sensing segmentation frameworks, reaching the best results of 74.12% mIoU on Fine-RSMI dataset and 78.64% mAcc on WHDLD dataset. Fine-RSMI dataset and code will be available at https://github.com/chenmu1204/czx.

Hyperspectral remote sensing images provide rich spectral information about land surface features and are widely used in fields such as environmental monitoring, disaster assessment, and land classification. However, effectively leveraging the spectral information in hyperspectral images remains a significant challenge. In this paper, we propose a hyperspectral pansharpening method based on ratio transformation and residual networks, which significantly enhances both spatial details and spectral fidelity. The method generates an initial image through ratio transformation and refines it using a residual attention network. Additionally, specialized loss functions are designed to preserve both spatial and spectral details. Experimental results demonstrate that, when evaluated on the EO-1 and Chikusei datasets, the proposed method outperforms other methods in terms of both visual quality and quantitative metrics, particularly in spatial detail clarity and spectral fidelity. This approach effectively addresses the limitations of existing technologies and shows great potential for high-resolution remote sensing image processing applications.

This study introduces the Feature-Enhanced Convolutional Attention Integration (FEC-AI) module, an innovative convolutional neural network (CNN) algorithm specifically tailored for the meticulous detection of small, challenging objects in high-resolution remote sensing imagery, emphasizing unstable rock formation monitoring. FEC-AI significantly advances CNN-based feature extraction and representation through a combination of advanced techniques. These include the Cross-Layer Attention Module, which enriches feature maps with multi-scale contextual details; the Offset-aware Adjustment Module for precise spatial refinement of feature localization; and the Contextual Feature Aggregation process, which synergizes these refined features for enhanced detection efficacy. Concurrently, we introduce the RSUR-2D dataset, a comprehensive compilation of 1,557 rigorously annotated images depicting karst landscapes around Beijing, expressly designed to challenge and advance remote sensing algorithms in geological hazard analysis. Through extensive testing on the RSUR-2D and established COCO datasets, the FEC-AI module demonstrated outstanding performance in small object detection, achieving a mean Average Precision at mAP ₅₀ of 66.0% and mAP _s of 21.1% on the RSUR-2D dataset. The RSUR-2D dataset, a valuable resource for the research community, is publicly accessible at https://github.com/chenmu1204/czx.