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Heterogeneous high-resolution remote sensing image matching will be disturbed by the differences in sensor type, imaging angle, height, and imaging time, and the matching difficulty is further increased in complex scenes with dense urban buildings and noticeable height differences. This article proposes a method for matching heterogeneous high-resolution remote sensing images based on partitioned feature extraction and three-dimensional spatial constraints. First, this article conducts image partitioning based on the geometric differences of ground objects. Two feature extraction methods, namely adaptive phase threshold and weighted moment map, are employed to extract feature points independently. To address the issue of inaccurate feature descriptions caused by drastic changes in viewing angles in buildings, we construct a robust feature descriptor by combining a multiscale phase weighted energy convolution histogram with a new gradient location orientation histogram-like local feature descriptor. In addition, a new similarity measure incorporating three-dimensional spatial constraints and the marginalizing sample consensus method is applied to eliminate mismatched point pairs, ensuring the acquisition of precise matching points. Based on the feature detection results of two different synthetic data sets, it is evident that the proposed detector outperforms the three classical detectors in terms of repeatability and uniformity. Ultimately, the matching performance is experimentally verified on six groups of heterogeneous high-resolution remote sensing images. The experimental results show that the proposed method significantly outperforms RIFT, HAPCG, and MS-HLMO methods and achieves the best matching accuracy results.

Differences in sensor types, resolutions, and imaging conditions can lead to considerable spectral differences in heterogeneous optical remote sensing images and the similarity of scale-invariant feature transform (SIFT) or local self-similarities (LSS) feature descriptors of the same point can be poor. Consequently, we proposed a robust descriptor construction algorithm considering the changing gray values inside ground objects. The main contributions of this article include the following. First, based on the stability of the internal gray value changes of ground objects, we suggest that the change orientations and degrees of gray values of pixels can be used to express the stability of the same area of heterogeneous images, providing the basis for image matching; second, unlike many existing methods that use gradient information to calculate feature orientation and descriptors, the proposed algorithm uses change orientation and degree to calculate the feature orientation and descriptor, enabling it to obtain stable descriptors in image matching with large illumination changes. Experimental analysis of homologous and heterogeneous optical remote sensing images demonstrated the superior stability and capability of the proposed algorithm over commonly used algorithms, including the radiation-invariant feature transform, adaptive binning SIFT, gradient orientation modification SIFT, and LSS algorithms.

With the development of deep learning, convolutional neural networks (CNNs) have been successfully applied in the field of change detection in heterogeneous remote sensing (RS) images and achieved remarkable results. However, most of the existing methods of heterogeneous RS image change detection only extract deep features to realize the whole image transformation and ignore the description of the topological structure composed of the image texture, edge, and direction information. The occurrence of change often means that the topological structure of the ground object has changed. As a result, these algorithms severely limit the performance of change detection. To solve these problems, this paper proposes a new topology-coupling-based heterogeneous RS image change detection network (TSCNet). TSCNet transforms the feature space of heterogeneous images using an encoder–decoder structure and introduces wavelet transform, channel, and spatial attention mechanisms. The wavelet transform can obtain the details of each direction of the image and effectively capture the image’s texture features. Unnecessary features are suppressed by allocating more weight to areas of interest via channels and spatial attention mechanisms. As a result of the organic combination of a wavelet, channel attention mechanism, and spatial attention mechanism, the network can focus on the texture information of interest while suppressing the difference of images from different domains. On this basis, a bitemporal heterogeneous RS image change detection method based on the TSCNet framework is proposed. The experimental results on three public heterogeneous RS image change detection datasets demonstrate that the proposed change detection framework achieves significant improvements over the state-of-the-art methods.