A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Abstract

Non-agriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intra-class differences of cropland changes in high-resolution images (HRIs). In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, we propose a CNN-transformer network with multi-scale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland change detection. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based multi-scale context aggregator (MSCA) is designed to encode and aggregate context information. Finally, a multi-branch prediction head (MBPH) with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of change detection dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset (CLCD), which contains 600 pairs of 512×512 bi-temporal images with the spatial resolution of 0.5-2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic change detection dataset (HRSCD), and of 71.29% on CLCD. Code and dataset in the paper will be available for download from the following link https://github.com/liumency/CropLand-CD.