STENet: A Spatial Selection and Temporal Evolution Network for Change Detection in Remote Sensing Images

Accompanied by the booming development of remote sensing (RS) imaging techniques, change detection (CD) has emerged as a conspicuous focal point in the realm of geoscience. Traditionally, extensive research has predominantly centered on extracting semantic features from individual images yet neglecting the interinput correlations latent in bitemporal imagery. This oversight gives rise to the occurrence of pseudovariant regions and the blurring of detection boundaries. To address these challenges, we propose a spatial selection and temporal evolution network, named STENet, which aims to unravel semantic correlations between bitemporal images from both spatial and temporal perspectives. Specifically, a dual pathway is crafted. The first one explores precisely the spatial localization of changes in bitemporal pairs, while the other complements the local details by generating a pseudovideo input via specific data augmentation. To boost the precision (Pre) of localizing sparsely changed targets, we further present a dynamic selective attention (DSA) mechanism, which strives for more focus on the positive regions while holding a mild computational demand. Moreover, by leveraging data augmentation to derive the temporal evolution of a set of progressively changed images, we then exploit a 3-D convolution-based encoder to mine the potential details therein, endeavoring to a refinement of target boundaries. Ultimately, STENet enforces the fusion of multiscale spatial and temporal features through a dedicated decoder and generates the final change map. Experiments on four popular datasets show that our proposal scores higher than most state-of-the-art approaches. In addition, the appealing performance is achieved with mild quantities of parameters and computations. The code is available at https://github.com/ZhengJianwei2/STENet.