Land-Cover Classification With High-Resolution Remote Sensing Images Using Interactive Segmentation

Deep convolutional neural network (CNN) has been increasingly applied in interpretation of remote sensing image such as automatically mapping land cover. Although the automatic CNN method achieves relatively high accuracy, there are still many misclassified areas. Considering that it is still far from practical application, this paper proposes a semi-automatic auxiliary scheme for land cover classification whose core idea is to use an interactive segmentation network. To infer the rough positions and categories of objects, a CNN is relied on to classify images in a patch-wise manner. Then an interactive segmentation method is proposed by accepting user-clicks on the inside and outside of object to guide the model for the segmentation task in the patches. This model also introduces different interactive modules to better integrate features of different scales. In addition, we create a large-scale sample library containing five common land cover categories which covers Jiangsu Province, China, and includes both aerial and satellite imagery. On our sample, we gave a thorough evaluation of most recent deep learning-based methods. The experimental results shown by our interactive segmentation also far outperform the recent semantic segmentation method, which provides a reference for semi-automatic land cover mapping.