Semantic change detection (SCD) has gradually emerged as a prominent research focus in remote sensing image processing due to its critical role in earth observation applications. In view of its powerful semantic-driven feature extraction capability, the Segment Anything Model (SAM) has demonstrated its suitability across various visual scenes. However, it suffers from significant performance degradation when confronted with remote sensing images, especially those containing various ground objects that possess significant inter-class similarity and substantial intra-class variations. To address the above issues, we propose SCD-SAM, aiming to leverage the potent visual recognition capabilities of SAM for enhanced accuracy and robustness in SCD. Specifically, we introduce a contextual semantic change-aware dual encoder that combines MobileSAM and CNN to extract progressive semantic change features in parallel, and inject local features into the MobileSAM encoder through depth feature interaction to compensate for the Transformer’s limitations in perceiving local semantic details. Besides, in order to utilize the strong visual feature extraction capability of MobileSAM in remote sensing images, we propose a semantic adaptor that aggregates semantic-oriented information about changing objects. To better integrate the extracted contextual semantic information, we devise a progressive feature aggregation dual decoder that aggregates binary change features and semantic change features respectively, alleviating the semantic gap across different scales. The quantitative and visual results show that SCD-SAM outperforms the state-of-the-art SCD methods on publicly open SCD datasets (e.g., SECOND-CD and Landsat-CD). The code will be made available at https://github.com/yzygit1230/SCD-SAM.

Building change detection (BCD) is essential for urban dynamic measurement. Deep learning has demonstrated significant potential in image processing, providing powerful feature extraction capabilities for BCD tasks. However, existing methods do not adequately mine multiscale feature information and ignore the importance of multiscale feature alignment, leading to an inadequate representation of the internal structure. Therefore, we propose a hybrid attention-aware transformer network (HATNet) designed to effectively extract and interact with multiscale context information. Specifically, HATNet first incorporates a hybrid attention-aware feature extractor (HAFE) module that integrates self-attention (SA) and coordinate-attention (CA) to effectively extract complementary multiscale features. The SA establishes long-range dependencies between multiscale features, while the CA captures spatial dependencies and preserves positional details. Then, we devise a building saliency detection enhancement (BSDE) module that utilizes three independent channels to facilitate the identification and localization of changed buildings, fostering a symbiotic relationship between unchanged and changed areas. Furthermore, we adopt a coarse-to-fine feature interaction (CFFI) module to progressively fuse multiscale features using a hierarchical strategy. In order to better locate the changed detail features, we introduce a global feature alignment (GFA) module to achieve global multiscale feature alignment. HATNet surpasses eight BCD methods on LEVIR-CD, WHU-CD, and S2Looking-CD datasets. The experimental results provide compelling evidence of our method to precisely detect and measure building changes. The codes are available at https://github.com/yzygit1230/HATNet .

Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020.

To solve the matching problems caused by the large intensity difference between the multi-source images and the nonlinear radiation distortion, we present a multi-source image matching approach that considers the orientation of the phase sharpness. First, the scale-space of the image pyramid was constructed, and the phase consistency of the image frequency domain was solved to obtain the maximum moment feature, and the KAZE operator is used to extract the feature points. Next, the Log-Gabor even symmetric filter was used to perform Fourier transform, and the improved local phase sharpness feature and phase orientation feature were constructed respectively to replace the gradient amplitude and gradient direction feature of the image. A descriptor of local phase sharpness orientation (LPSO) was established through the log-polar description template, and finally the Euclidean distance was used to measure the similarity to obtain the corresponding points. Multiple sets of typical multi-source images were used as data sources, and the LPSO algorithm was compared with SIFT, LGHD, RIFT and HAPCG algorithms in datasets. Evaluation of the algorithm performances indicates that the proposed method is more accurate and robust in the task of multi-source image matching.

Building change detection (BCD) using high-resolution remote sensing images aims to identify change areas during different time periods, which is a significant research focus in urbanization. Deep learning methods are capable of yielding impressive BCD results by correctly extracting change features. However, due to the heterogeneous appearance and large individual differences of buildings, mainstream methods cannot further extract and reconstruct hierarchical and rich feature information. To overcome this problem, we propose a progressive context-aware aggregation network combining multi-scale and multi-level dense reconstruction to identify detailed texture-rich building change information. We design the progressive context-aware aggregation module with a Siamese structure to capture both local and global features. Specifically, we first use deep convolution to obtain superficial local change information of buildings, and then utilize self-attention to further extract global features with high-level semantics based on the local features progressively, which ensures capability of the context awareness of our feature representations. Furthermore, our multi-scale and multi-level dense reconstruction module groups extracted feature information according to pre- and post-temporal sequences. By using multi-level dense reconstruction, the following groups are able to directly learn feature information from the previous groups, enhancing the network’s robustness to pseudo changes. The proposed method outperforms eight state-of-the-art methods on four common BCD datasets, including LEVIR-CD, SYSU-CD, WHU-CD, and S2Looking-CD, both in terms of visual comparison and objective evaluation metrics.

Road damage detection is essential to the maintenance and management of roads. The morphological road damage contains a large number of multi-scale features, which means that existing road damage detection algorithms are unable to effectively distinguish and fuse multiple features. In this paper, we propose a dense multiscale feature learning Transformer embedding cross-shaped attention for road damage detection (DMTC) network, which can segment the damage information in road images and improve the effectiveness of road damage detection. Our DMTC makes three contributions. Firstly, we adopt a cross-shaped attention mechanism to expand the perceptual field of feature extraction, and its global attention effectively improves the feature description of the network. Secondly, we use the dense multi-scale feature learning module to integrate local information at different scales, so that we are able to overcome the difficulty of detecting multiscale targets. Finally, we utilize a multi-layer convolutional segmentation head to generalize the previous feature learning and get a final detection result. Experimental results show that our DMTC network could segment pavement pothole patterns more accurately and effectively than other methods, achieving an F1 score of 79.39% as well as an OA score of 99.83% on the cracks-and-potholes-in-road-images-dataset (CPRID).