Underwater image enhancement (UIE) aims to address image degradation caused by water absorption and scattering effects. Despite significant progress in deep learning-based UIE methods, existing approaches still face key challenges due to the neglect of physical imaging principle. Moreover, while current Mamba models achieve global modeling via multi-directional scanning, their local sequential strategy lacks sufficient global context. To this end, we propose a novel Physical Model-Guided Global Mamba (PGMamba) that combines the efficient sequential modeling capability of Mamba with underwater imaging physical model. Specifically, we first design a Spatial-Aware Global Mamba (SAGMamba) that achieves efficient long-range dependency modeling through a spatial-aware ranking strategy with global context information. Second, we develop a Physical Model-Guided Feed-Forward Network (PMGFFN) that explicitly incorporates underwater optical imaging principles into the network architecture. Extensive experimental results and comprehensive ablation studies demonstrate the outstanding performance and importance of our proposed method.

Change detection (CD) is one of the prominent research topics in the fields of Earth science and remote sensing. Recently, an increasing number of deep learning-based CD methods have been developed. Most of the current CD methods require lots of pixel-level labels for supervised learning. However, annotating all the changed pixels in bitemporal images is both challenging and time-consuming. In this work, as a first attempt in the field of CD, we propose a novel CD framework, similarity-awareness weakly supervised CD (SWCD) to achieve accurate CD, which uses weakly supervised learning as an auxiliary task to guide the model in both semi-supervised and supervised learning. In the weakly supervised branch (WSB), we incorporate the concept of similarity and introduce similarity information into the supervised branch to guide pixel-level CD learning, thus enhancing feature continuity. Moreover, large kernel convolution attention is introduced to enhance multiscale feature learning. In the supervised branch, we re-evaluate the approach to multiscale feature aggregation and introduce an adaptive feature module to integrate features from both global and local perspectives. Furthermore, our method can serve as a general framework that is compatible with the existing CD approaches. Experimental results on four CD datasets demonstrate the superior effectiveness and generalization of our proposed method. The code is available athttps://github.com/ZijunTan/SWCD.

Remote sensing change detection (RSCD) focuses on identifying regions that have undergone changes between two remote sensing images captured at different times. Recently, convolutional neural networks (CNNs) have shown promising results in the challenging task of RSCD. However, these methods do not efficiently fuse bitemporal features and extract useful information that is beneficial to subsequent RSCD tasks. In addition, they did not consider multilevel feature interactions in feature aggregation and ignore relationships between difference features and bitemporal features, which thus affects the RSCD results. To address the above problems, a difference-guided multiscale aggregation attention network, DGMA2-Net, is developed. Bitemporal features at different levels are extracted through a Siamese convolutional network and a multiscale difference fusion module (MDFM) is then created to fuse bitemporal features and extract, in a multiscale manner, difference features containing rich contextual information. After the MDFM treatment, two difference aggregation modules (DAMs) are used to aggregate difference features at different levels for multilevel feature interactions. The features through DAMs are sent to the difference-enhanced attention modules (DEAMs) to strengthen the connections between bitemporal features and difference features and further refine change features. Finally, refined change features are superimposed from deep to shallow and a change map is produced. In validating the effectiveness of DGMA2-Net, a series of experiments are conducted on three public RSCD benchmark datasets (LEVIR-CD, BCDD, and SYSU-CD). The experimental results demonstrate that DGMA2-Net surpasses the current eight state-of-the-art methods in RSCD. Our code is released at https://github.com/yikuizhai/DGMA2-Net.

Change detection (CD) is a process of extracting changes on the Earth’s surface from bitemporal images. Current CD methods that use high-resolution remote sensing images require extensive computational resources and are vulnerable to the presence of irrelevant noises in the images. In addressing these challenges, a comparison-based attention Siamese network (CAS-Net) is proposed. The network utilizes contrastive attention modules (CAMs) for feature fusion and employs a classifier to determine similarities and differences of bitemporal image patches. It simplifies pixel-level CDs by comparing image patches. As such, the influences of image background noises on change predictions are reduced. Along with the CAS-Net, an unmanned aerial vehicle (UAV) similarity detection (UAV-SD) dataset is built using high-resolution remote sensing images. This dataset, serving as a benchmark for CD, comprises 10000 pairs of UAV images with a size of$256 \times 256$. Experiments of the CAS-Net on the UAV-SD dataset demonstrate that the CAS-Net is superior to other baseline CD networks. The CAS-Net detection accuracy is 93.1% on the UAV-SD dataset. The code and the dataset can be found athttps://github.com/WenbaLi/CAS-Net.

Remote sensing change detection (RSCD) holds significant prominence as a research topic within the realm of computer vision. However, previous RSCD datasets have been constructed based on satellite remote sensing images. Traditional satellite remote sensing images have problems such as insufficient resolution, difficult data acquisition, and complex processing processes, and there is a certain gap between data distribution and actual needs. UAVs not only have the advantages of flexibility and high-speed, but also can capture high-resolution images, which are especially suitable for high-precision RSCD in small areas. Therefore, this paper proposed a new UAV RSCD dataset — UAV Building Change Detection Dataset (UAV-BCD). The proposed dataset contains 2024 pairs of finely registered high-resolution images collected by UAVs and their corresponding pixel-level labels, which can provide a new benchmark for RSCD. We evaluate the effectiveness of UAV-BCD with the five state-of-art deep neural networks in RSCD.

Change detection refers to extract change information using deep learning or traditional image processing methods to quantitatively analyze and characterize landmark changes on bi-temporal images. Currently, change detection is mainly a pixel-level task, and obtaining accurate change detection segmentation predictions requires a more elaborate and complex model architecture design. To simplify the change detection task, we proposed a novel similarity detection model, Similarity Attention Siamese Network (SAS-NET). It analyzed and predicted if the bi-temporal image patches were similar, and simplified pixel-level change detection tasks to patch-level similarity classification prediction tasks. In this work, a UAV Similarity Detection Dataset (UAV-SD) was also proposed to explore the advantages of patch-level prediction tasks over pixel-level change detection tasks. The proposed method achieved 90.5% accuracy on UAV-SD, which proves that it is more effective than other advanced change detection methods.