Journal
JOURNAL OF APPLIED REMOTE SENSING
Volume 13, Issue 1, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JRS.13.016501
Keywords
semantic segmentation; multisensor remote sensing; light detection and ranging; deep convolutional neural networks; conditional random fields
Funding
- Department of Defense
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Aerial images acquired by multiple sensors provide comprehensive and diverse information of materials and objects within a surveyed area. The current use of pretrained deep convolutional neural networks (DCNNs) is usually constrained to three-band images (i.e., RGB) obtained from a single optical sensor. Additional spectral bands from a multiple sensor setup introduce challenges for the use of DCNN. We fuse the RGB feature information obtained from a deep learning framework with light detection and ranging (LiDAR) features to obtain semantic labeling. Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data. Our approach first obtains initial probabilistic predictions from two different sources: one from a pretrained neural network fine-tuned on a three-band optical image, and another from a probabilistic classifier trained on LiDAR data. These two predictions are then combined as the unary potential using a higher-order conditional random field (CRF) framework, which resolves fusion ambiguities by exploiting the spatial-contextual information. We utilize graph cut to efficiently infer the final semantic labeling for our proposed higher-order CRF framework. Experiments performed on three benchmarking multisensor datasets demonstrate the performance advantages of our proposed method. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.
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