Instance segmentation of remote sensing images can not only provide object-level positioning information but also provide pixel-level positioning information. This pixel-level information annotation has a wide range of uses in the field of remote sensing, and it is of great value for environmental detection and resource management. Because optical images generally have complex terrain environments and changeable object shapes, SAR images are affected by complex scattering phenomena, and the mask quality obtained by the traditional instance segmentation method used in remote sensing images is not high. Therefore, it is a challenging task to improve the mask quality of instance segmentation in remote sensing images. Since the traditional two-stage instance segmentation method consists of backbone, neck, bbox head, and mask head, the final mask quality depends on the product of all front-end work quality. Therefore, we consider the difficulty of optical and SAR images to bring instance segmentation to the targeted improvement of the neck, bbox head, and mask head, and we propose the Context-Aggregated and SAM-Guided Network (CSNet). In this network, the plain feature fusion pyramid network (PFFPN) can generate a pyramid for the plain feature and provide a feature map of the appropriate instance scale for detection and segmentation. The network also includes a context aggregation bbox head (CABH), which uses the context information and instance information around the instance to solve the problem of missed detection and false detection in detection. The network also has a SAM-Guided mask head (SGMH), which learns by using SAM as a teacher, and uses the knowledge learned to improve the edge of the mask. Experimental results show that CSNet significantly improves the quality of masks generated under optical and SAR images, and CSNet achieves 5.1% and 3.2% AP increments compared with other SOTA models.

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

Forest fires occur frequently in China; however, the complex terrain and incomplete road networks severely constrain ground rescue efficiency. Accurate forest road information is essential for the optimization of emergency response and rescue force deployment. Existing road extraction algorithms are primarily designed for urban environments and exhibit limited efficacy in forest scenarios due to dense canopy, complex background interference and specific forest road features. To address this gap, this study proposes a forest road extraction method based on an enhanced DeepLabv3+ model using multi-temporal, high-resolution satellite imagery. Specifically, a Multi-Scale Channel Attention (MCSA) mechanism is embedded in skip connections to suppress background interference, while strip pooling is integrated into the Atrous Spatial Pyramid Pooling (ASPP) module to better capture slender road features. A composite Focal-Dice loss function is also constructed to mitigate sample imbalance. Finally, by applying the model in multi-temporal remote sensing images, a fusion strategy is introduced to integrate multi-seasonal road masks to enhance overall accuracy and topological integrity. Experimental results show that the proposed method achieves a precision of 54.1%, an F1-Score of 59.3%, and an IoU of 41.8%, effectively enhancing road continuity and providing robust technical support for fire-rescue decision-making.