The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcomes the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at https://github.com/ChengxiHAN/CGNet-CD .

Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP. The codes and pretrained models will be released at https://github.com/ViTAE-Transformer/MTP .

The application of convolutional neural networks has been shown to significantly improve the accuracy of building extraction from very high-resolution (VHR) remote sensing images. However, there exist so-called semantic gaps among different kinds of buildings due to the large intraclass variance of buildings, and most of the present-day methods are ineffective in extracting various buildings in large areas that cover different scenes, for example, urban villages and high-rise buildings, because existing building extraction strategies are the same for various scenes. With the improvement of the resolution of remote sensing images, it is feasible to improve the image interpretation based on the scene prior. However, this idea has not been fully utilized in building extraction from VHR remote sensing imagery. This study proposes a scene-driven multitask parallel attention convolutional network (MTPA-Net) to resolve these limitations. The proposed approach classifies the input image into multilabel scenes and further separately maps the buildings in pixel level under different scenes. In addition, a simple postprocessing method is applied to integrate the building extraction results and scene prior. Our proposed method does not require multimodel training and the network can learn in an end-to-end manner. The performance of our proposed method is evaluated on a data set that includes various urban and rural scenes with diverse landscapes. The experimental results show that the proposed MTPA-Net outperforms state-of-the-art algorithms by reducing misclassification areas and maintaining improved robustness.

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models’ confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.

Very high-resolution (VHR) earth observation systems provide an ideal data source for man-made structure detection such as building footprint extraction. Manually delineating building footprints from the remotely sensed VHR images, however, is laborious and time-intensive; thus, automation is needed in the building extraction process to increase productivity. Recently, many researchers have focused on developing building extraction algorithms based on the encoder-decoder architecture of convolutional neural networks. However, we observe that this widely adopted architecture cannot well preserve the precise boundaries and integrity of the extracted buildings. Moreover, features obtained by shallow convolutional layers contain irrelevant background noises that degrade building feature representations. This paper addresses these problems by presenting a feature decoupling network (FD-Net) that exploits two essential building information from the input image, including semantic information that concerns building integrity and edge information that improves building boundaries. The proposed FD-Net improves the existing encoder-decoder framework by decoupling image features into the edge subspace and the semantic subspace; the decoupled features are then integrated by a supervision-guided fusion process considering the heterogeneity between edge and semantic features. Furthermore, a lightweight and effective global context attention module is introduced to capture contextual building information and thus enhance feature representations. Comprehensive experimental results on three real-world datasets confirm the effectiveness of FD-Net in large-scale building mapping. We applied the proposed method to various encoder-decoder variants to verify the generalizability of the proposed framework. Experimental results show remarkable accuracy improvements with less computational cost.

Sudden natural disasters and man-made disasters pose a threat to human life and property safety, and real-time semantic segmentation of high-resolution remote sensing images is crucial for disaster damage assessment applications. In recent years, with the wide application of high spatial resolution (HSR) remote sensing images and semantic change detection methods based on deep learning (DL), the acquisition of information on damaged areas has become more and more convenient and accurate. However, due to the black box characteristics of existing methods, the lack of interpretability and prior knowledge embedding (such as building positioning information), as well as the low utilization of damage conditions around the building, lead to automatically learned feature representations that still need to be improved. To solve these problems, we proposed the position-constrained differential attention Siamese network (PCDASNet). The main idea is to merge building extraction and disaster damage assessment into a cascaded framework to improve building damage recognition results under the constraints of building positioning information. In particular, the proposed differential attention module (DAM) adaptively extracts change information corresponding to buildings and surrounding environments from dual-temporal images, with interpretability and theoretical guarantee, which enables the integration of prior positioning knowledge into the design of network architecture. The objective metrics of the method on the building damage dataset show that the method achieves a test F1 score of more than 73% compared with other baseline methods, and also outperforms several state-of-the-art methods in terms of visual results.