Different Modality Based Remote Sensing Data Fusion Approach for Efficient Classification of Agriculture and Urban Subclasses

Subclasses classification is one of the major challenges in remote sensing (RS) scene classification. The area under observation, in order to classify agriculture and urban subclasses, requires efficient classification algorithms. Among such algorithms, deep learning algorithm based on Convolutional Neural Network (CNN) architecture is one such promising candidate to obtain the classified map. In this work, performance of a CNN network has been demonstrated on the data obtained from National Ecological Observatory Network (NEON) field site Domain 17 by considering different modality data and its subsequent fusion using the proposed model of CNN as applied on (i) the Hyperspectral, (ii) the Light Detection and Ranging (LiDAR) and then (iii) fused data respectively. Both the Hyperspectral and the LiDAR data have been fused at pixel level. Using the proposed methodology, a classified map is obtained with an overall accuracy of 96 percent for fused data.