CNN-BASED FEATURE-LEVEL FUSION OF VERY HIGH RESOLUTION AERIAL IMAGERY AND LIDAR DATA

S. Daneshtalab, H. Rastiveis, B. Hosseiny
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Abstract. Land-cover classification of Remote Sensing (RS) data in urban area has always been a challenging task due to the complicated relations between different objects. Recently, fusion of aerial imagery and light detection and ranging (LiDAR) data has obtained a great attention in RS communities. Meanwhile, convolutional neural network (CNN) has proven its power in extracting high-level (deep) descriptors to improve RS data classification. In this paper, a CNN-based feature-level framework
more » ... is proposed to integrate LiDAR data and aerial imagery for object classification in urban area. In our method, after generating low-level descriptors and fusing them in a feature-level fusion by layer-stacking, the proposed framework employs a novel CNN to extract the spectral-spatial features for classification process, which is performed using a fully connected multilayer perceptron network (MLP). The experimental results revealed that the proposed deep fusion model provides about 10% improvement in overall accuracy (OA) in comparison with other conventional feature-level fusion techniques.
doi:10.5194/isprs-archives-xlii-4-w18-279-2019 fatcat:w3hzfgmrdnc63m7kvs2zcpm5fq