The Segment Anything Model (SAM) has had a profound impact on deep learning applications in remote sensing. SAM, which serves as a prompt-based foundation model for segmentation, exhibits a remarkable capability to “segment anything,” including building objects on satellite or airborne images. To facilitate building segmentation without inducing supplementary prompts or labels, we applied a sequential approach of generating pseudo-labels and incorporating an edge-driven model. We first segmented the entire scene by SAM and masked out unwanted objects to generate pseudo-labels. Subsequently, we employed an edge-driven model designed to enhance the pseudo-label by using edge information to reconstruct the imperfect building features. Our model simultaneously utilizes spectral features from SAM-oriented building pseudo-labels and edge features from resultant images from the Canny edge detector and, thus, when combined with conditional random fields (CRFs), shows capability to extract and learn building features from imperfect pseudo-labels. By integrating the SAM-based pseudo-label with our edge-driven model, we establish an unsupervised framework for building segmentation that operates without explicit labels. Our model excels in extracting buildings compared with other state-of-the-art unsupervised segmentation models and even outperforms supervised models when trained in a fully supervised manner. This achievement demonstrates the potential of our model to address the lack of datasets in various remote sensing domains for building segmentation.

Stereo matching algorithms are considered one of the most important sub-tasks in 3D reconstruction, as 3D coordinates are derived from the disparity values of pixels obtained through stereo matching. Recently, deep learning-based satellite stereo matching algorithms have been widely investigated, as they can capture both deep and shallow features of complex satellite scenes. However, several problems in satellite stereo matching, due to the unique properties of satellite images, remain unsolved, particularly in textureless and repetitive regions. In these regions, a single object in a satellite image is likely to be matched with similar objects, causing multiple disparity probabilities and shifts in the disparity estimation. To address the problem of disparity shifts, we propose a novel cost volume refinement strategy (CVRS). CVRS introduces both left-right and left-left cost volumes, which work together to refine disparities and eliminate false matches in textureless or repetitive regions, while preserving the original disparity values. With CVRS, we propose a new model for satellite stereo matching, the self-refined cost volume network (SRCV-Net). We evaluated CVRS and SRCV-Net on the US3D and WHU-Stereo datasets, comparing it using the EPE and D1 metrics. The application of CVRS demonstrated performance improvements in all models, and SRCV-Net achieved superior accuracy in satellite stereo matching. Furthermore, CVRS can be easily applied to various models with minimal structural changes and a small increase in parameters. SRCV-Net, with its innovative CVRS, provides an effective solution to the challenges of satellite stereo matching, offering enhanced accuracy, efficiency, and adaptability.

Yeseok Lee, Donghyeon Lee, Jiyong Kim, Yongil Kim. Korean J. Remote Sens. 2024;40:1209-17. https://doi.org/10.7780/kjrs.2024.40.6.1.26

Donghyeon Lee, Jiyong Kim, Yongil Kim. Korean J. Remote Sens. 2024;40:551-68. https://doi.org/10.7780/kjrs.2024.40.5.1.11