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

Daneshtalab, S.; Rastiveis, H.; Hosseiny, B.

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 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.

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Daneshtalab, S. / Rastiveis, H. / Hosseiny, B.: CNN-BASED FEATURE-LEVEL FUSION OF VERY HIGH RESOLUTION AERIAL IMAGERY AND LIDAR DATA. 2019. Copernicus Publications.

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Rechteinhaber: S. Daneshtalab et al.

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