Research on the Spatiotemporal-Coupled High-Resolution Remote Sensing Land Use Classification Method

Abstract

Highlights: What are the main findings? A “spatiotemporal coupling” classification paradigm is proposed, which effectively mitigates the performance degradation in high-resolution remote sensing image classification caused by temporal differences through temporal segmentation and dedicated feature extractor strategies. A parcel-oriented deep texture feature extraction and quantification method is designed and implemented, transforming pixel-level features into statistically descriptive vectors with clear geographical significance, significantly enhancing feature discriminability and interpretability. What are the implications of the main findings? This framework provides a scalable solution for fine-grained land cover recognition using multi-temporal, high-resolution remote sensing imagery, particularly suitable for agricultural areas with significant seasonal surface cover variations. The proposed technical workflow of “feature extraction–entity constraint–statistical quantification” promotes the effective integration of deep learning models with Geographic Information System (GIS) management needs, offering practical reference value for real-world applications. High-spatial-resolution remote sensing imagery provides a data foundation for fine-grained land use classification. However, due to long revisit cycles and susceptibility to cloud cover, large-area imagery often suffers from temporal inconsistency, which severely limits the classification accuracy of traditional unified models. To address this issue, this study proposes a geographic entity-oriented, spatiotemporally coupled land use classification method for high-resolution remote sensing imagery, with agricultural land (including paddy fields, dry farmland and gardens) as an example for validation. In this method, the study area is first divided into multiple sub-regions based on image acquisition time, ensuring temporal consistency within each sub-region. A dedicated deep texture feature extraction model is then constructed for each sub-region. This model is adapted from the advanced CAPTN texture recognition network: its classification head is removed, and a multi-scale feature fusion module is introduced, transforming it into an encoder focused on extracting spatial texture feature maps. Additionally, a self-supervised loss function combining masked feature reconstruction and cross-view consistency is designed to improve the quality of the learned texture features. During the prediction stage, the corresponding feature extractor is invoked based on the temporal phase of the imagery to generate a full-region texture feature map. This feature map is then cropped using land parcel vectors, and statistical feature vectors describing the texture attributes of each parcel are formed by calculating the mean and standard deviation of the features within each parcel. Finally, a Random Forest classifier is employed to determine the land parcel categories. This study uses the Jiangjin District of Chongqing City as the experimental area. The results show that, compared to training a unified deep learning model directly on full-region multi-temporal imagery or using traditional texture features, the proposed spatiotemporally coupled classification framework achieves significant improvements in overall accuracy and Kappa coefficient, reaching 92.3% and 0.89, respectively.