Panchromatic Image Land Cover Classification Via DCNN with Updating Iteration Strategy

Abstract

Land cover classification is a critical research task in many significant remote sensing applications. There are emerging many powerful pixel-level classification methods based on deep convolutional neural network (DCNN) in the universal computer vision community. However, due to the complication of satellite image senses and the lack of high-quality labeled datasets, these computer vision techniques can not be applied to remote sensing applications directly. In this paper, we propose a novel DCNN method to extract abstract feature from the complicated remote scene. The proposed method fuses three level features from the encoder while the segmentation result is obtained by decoder. Furthermore, we propose an updating iteration strategy (UIS) with label smoothing on training set to reduce the impact of the incorrect labels. The proposed strategy employs the output of the network to update the low-confidence labels on training set, and utilizes the updated labels to continue training the network. In order to acquire a better segmentation result on a very high resolution (VHR) panchromatic image, we transfer the features trained on GID dataset to our dataset for training. Our experiments has demonstrated the oustanding performance of the proposed method in land cover classification compared to DeepLabv3 on the GID and our dataset.