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

Ido Faran; Nathan S. Netanyahu; Eli David; Maxim Shoshany; Fadi Kizel; Jisung Geba Chang; Ronit Rud

doi:10.1109/igarss.2019.8900186

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arXiv · 1911.10442

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

Ido Faran·Nathan S. Netanyahu·Eli David·Maxim Shoshany·Fadi Kizel·Jisung Geba Chang·Ronit Rud

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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 VENuS imagery.

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eess.IV cs.CV cs.LG cs.NE stat.ML

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Ido Faran, Nathan S. Netanyahu, Eli David, Maxim Shoshany, Fadi Kizel, Jisung Geba Chang, Ronit Rud. 2019-11-24. Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data. https://doi.org/10.1109/igarss.2019.8900186

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