Change detection (CD) aims to identify and locate changes in the same observed surface coverage area across bitemporal images. This technique has widespread applications in urban planning, land use, and disaster damage extraction. Deep learning-based CD methods typically use learnable encoders to map bitemporal images to a common domain distribution space, allowing for the discrimination and localization of change and invariant features. However, due to differences in imaging mechanisms, seasons, and shooting angles, a large number of pseudochanges may easily appear, affecting the accurate recognition of the domain distribution space. In addition, binary CD focuses solely on whether scene targets have changed, resulting in change labels that encompass a variety of different objects, thus increasing the significance of intraclass differences. To address the aforementioned issues, we propose a domain distribution reconstruction learning (DDRL) framework for binary CD, which effectively mitigates the problem of pseudochanges by detecting abnormal feature domain distributions. Specifically, DDRL first extracts multiscale features from bitemporal images using a Siamese cross-window self-attention module, achieving feature domain transformation from the original space. Subsequently, it employs a graph attention enhanced (GAE) module to improve the low-level domain distribution, enabling it to focus on change regions. In addition, DDRL utilizes a cross-domain feature contrastive learning (CFCL) module for reconstructive learning of high-level fused features. This process ensures that intraclass features are compact, while interclass features are dispersed within the high-level domain distribution, thereby significantly improving the domain distribution representation to discriminate pseudochanges. Experimental results show that the proposed DDRL performs excellently across multiple public datasets, surpassing mainstream methods and significantly improving CD performance. The source code will be made available athttps://github.com/yzygit1230/DDRL.

Significant challenges exist in the registration of multimodal images (MMIs) due to nonlinear radiation differences, variations in lighting, and interference from image noise. These issues often lead to unreliable similarity measurements and low accuracy in point matching during multimodal registration. To address these challenges, this letter introduces a novel MMI registration method based on adjacent self-similarity 3-D convolution (ASTC). The proposed method consists of three main steps: feature point extraction, where key points are uniformly extracted via the block-FAST method; ASTC salient feature construction, where a local adjacent self-similarity (ASS) model is employed to create multidimensional features; and feature structure enhancement, where a 3-D convolution is used for feature enhancement and finishing the process of image feature description. This letter evaluates the ASTC method against six sets of representative MMIs and compares it with six other algorithms. The results demonstrate that: 1) the ASTC algorithm effectively overcomes radiation distortion, intensity differences, and lighting differences in MMIs, leading to improved accuracy in point matching; and 2) the ASTC algorithm achieves higher matching efficiency and reduces time consumption, making it a practical choice for various data types. In summary, the proposed ASTC algorithm offers a robust solution for reliable registration of MMIs, addressing common challenges related to image differences and improving the overall accuracy of the process. The experimental data and code link used in this letter can be found athttps://github.com/yangwill81/ASTC.

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.

Multisource image matching is still a challenging task due to the significant nonlinear radiometric differences and scale variations. To address the problem, we present a novel phase congruency approach and hierarchical structure constraint strategy for multisource image matching. Specifically, we employ the phase congruency of image frequency domain in Gaussian scale space, and KAZE operator was used to detect feature point in the maximum moment space, which was obtained by the Fourier transform of the Log-Gabor even-symmetric filter. And then, extended phase features within the neighborhood region were generated, next feature description in the framework of polar coordinates was obtained. Finally, in the stage of image matching, we use the hierarchical structure constraint strategy for random sampling and verification. The experiments show that the proposed algorithm is superior to d2-NET, LGHD, RIFT, and other mainstream multisource image matching methods. Especially for scale change and rotation, the proposed algorithm provides a better way to describe the common features of multisource images, and results in a reliable and accurate matching for multisource image with obvious intensity and radiation differences.

Feature-based image matching is the key technology of computer vision and image understanding. Local invariant feature point matching is a classical and popular method for image matching, and the coarse error elimination method is often used to improve the accuracy of matching results. However, complex spatial transformation representation relations existed between images in the multi-object scene. The traditional coarse error elimination method is difficult to effectively remove the matching error by constructing a single transformation matrix. Thus, this paper proposes a coarse error elimination algorithm for multi-object image matching based on adaptive local space constraints. The main idea of this paper is to construct the adaptive size local space with prior knowledge of rough matching, and mine the affine constraints of the local space to remove outliers. Experimental results show the effectiveness of the proposed method, compared with six main coarse error elimination methods (AdLAM, NBCS, LLT, VFC, GLOF, RANSAC). And the proposed method can improve the matching accuracy by about 32.6% in this experiment.