Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data

GROUND TRUTH SIMULA TION FOR DEEP LEARNING CLASSIFICA TION OF MID-RESOLUTION VENUS IMAGES VIA UNMIXING OF HIGH-RESOLUTION HYPERSPECTRAL FENIX DA T A Ido Faran, Nathan S. Netanyahu Eli (Omid) David Bar-Ilan University Dept. of Computer Science ramat-gan 5290002, Israel Maxim Shoshany, Fadi Kizel Jisung Geba Chang, Ronit Rud Technion Israel Institute of Technology Faculty of Civil and Environmental Engineering Haifa 3200003, Israel ABSTRACT Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data. Acquiring high-resolution ground truth (GT) by human interpretation is both cost-ineffective and inconsistent. We propose, instead, to utilize high-resolution, hyperspectral images for solving this problem, by unmixing these images to obtain reliable GT for training a deep network. Specifically, we simulate GT from high-resolution, hyperspectral FENIX images, and use it for training a convolutional neural network (CNN) for pixel-based classification. We show how the model can be transferred successfully to classify new mid-resolution VENµ S imagery.