Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods fail to generate fine segmentation results, especially for the object boundary pixels. In order to obtain boundary-preserving predictions, we first propose to incorporate spatially adapting contextual cues. In this way, objects with similar appearance can be effectively distinguished with the extracted global contextual cues, which are very helpful to identify pixels near object boundaries. On this basis, low-level spatial details and high-level semantic cues are effectively fused with the help of our proposed dual attention mechanism. Concretely, when fusing multi-level features, we utilize the dual attention feature fusion module based on both spatial and channel attention mechanisms to relieve the influence of the large gap, and further improve the segmentation accuracy of pixels near object boundaries. Extensive experiments were carried out on the ISPRS 2D Semantic Labeling Vaihingen data and GaoFen-2 data to demonstrate the effectiveness of our proposed method. Our method achieves better performance compared with other state-of-the-art methods.
A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images
