Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs

TLDR: This paper proposes a decision-level fusion approach using a probabilistic graphical model for the task of dense semantic labeling of airborne remote sensing imagery and shows that the proposed approach compares favorably to the state-of-the-art baseline methods.

Abstract: The increasing availability of very-high-resolution (VHR) aerial optical images as well as coregistered Li- DAR data opens great opportunities for improving objectlevel dense semantic labeling of airborne remote sensing imagery. As a result, efficient and effective multisensor fusion techniques are needed to fully exploit these complementary data modalities. Recent researches demonstrated how to process remote sensing images using pre-trained deep convolutional neural networks (DCNNs) at the feature level. In this paper, we propose a decision-level fusion approach using a probabilistic graphical model for the task of dense semantic labeling. Our proposed method first obtains two initial probabilistic labeling predictions from a fully-convolutional neural network and a linear classifier, e.g. logistic regression, respectively. These two predictions are then combined within a higher-order conditional random field (CRF). We utilize graph cut inference to estimate the final dense semantic labeling results. Higher-order CRF modeling helps to resolve fusion ambiguities by explicitly using the spatial contextual information, which can be learned from the training data. Experiments on the ISPRS 2D semantic labeling Potsdam dataset show that our proposed approach compares favorably to the state-of-the-art baseline methods.