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

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

Authors :
Ido Faran
Eli David
Maxim Shoshany
Ronit Rud
Fadi Kizel
Jisung Geba Chang
Nathan S. Netanyahu
Source :
IGARSS
Publication Year :
2019
Publisher :
IEEE, 2019.

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.

Details

Database :
OpenAIRE
Journal :
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Accession number :
edsair.doi.dedup.....6dd73ed0eb78be0fd83d22ff04a98e7d
Full Text :
https://doi.org/10.1109/igarss.2019.8900186