Deep Fusion of Spectral–Spatial Priors for Cropland Segmentation in Remote Sensing Images

Cropland segmentation is one of the critical techniques in agriculture remote sensing (RS). Although the deep learning (DL) methods have achieved remarkable performance in natural vision, the cropland segmentation of RS images still suffers from cropland adhesion due to interference from the surrounding environment and the cropland cover. To tackle this problem, this letter proposes a two-stage DL method with spectral–spatial priors. In the first stage, the multifeature extraction module (MEM) is designed to predict the boundary, an important spatial prior of the cropland. In the second stage, the spatial prior is further fused with the spectral prior by MEMs to get accurate cropland prediction. To evaluate the effectiveness and robustness of the proposed method, we construct a dataset called Jiaxiang Cropland Set (JCS) and propose a region-level evaluation indicator namely the plot mean intersection over union (PMIoU). The experimental results on the JCS demonstrate that the proposed method is both qualitatively and quantitatively competitive compared with the state-of-the-art methods.