Semantic segmentation of remote sensing data using Gaussian processes and higher-order CRFS
Yansong Liu, Sankaranarayanan Piramanayagam, Sildomar T. Monteiro, Eli S. Saber · 2017
Automatic recognition for complex scenes from aerial images and other sensor data (e.g. LiDAR) has become an active topic in the remote sensing community. In this paper, we proposed a novel framework that utilizes higher-order CRFs (HCRFs) to capture the spatial contextual information for the RGB aerial images along with their co-registered LiDAR data (DSMs). Our proposed HCRFs framework exploits the spatial contextual information in two levels. The first level encourages harmonic label co-existence within one segment, which can be generated by an unsupervised superpixel algorithm. The second level takes into account the local object co-occurrence among adjacent segments. We then show that how to apply the move making graph cuts algorithm to perform efficient inference for our proposed CRFs framework. Based on the experiments on a challenging data set, our proposed higher-order CRF framework generated state-of-the-art semantic segmentation results.