SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation
Daiki Kimura, Tatsuya Ishikawa, Masanori Mitsugi, Yasunori Kitakoshi, Takahiro Tanaka, Naomi Simumba, Kentaro Tanaka, Hiroaki Wakabayashi
Abstract
Geospatial machine learning is of growing importance in various global remote-sensing applications, particularly in the realm of vegetation monitoring. However, acquiring accurate ground truth data for geospatial tasks remains a significant challenge, often entailing considerable time and effort. Foundation models, emphasizing pre-training on large-scale data and fine-tuning, show promise but face limitations when applied to geospatial data due to domain differences. Our paper introduces a novel image translation method, combining geospatial-specific pre-training with training and test-time data augmentation. In a case study involving the translation of normalized difference vegetation index (NDVI) values from synthetic aperture radar (SAR) images of cabbage farms, our approach outperformed competitors by 31% in a public competition. It also exceeded the average of the top five teams by 44%. We publish both our image translation method with baseline methods and the geospatial-specific dataset at https://github.com/IBM/SAR2NDVI.
BibTeX
@inproceedings{icassp2024_sar2ndvipretrain,
title = {SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation},
author = {Daiki Kimura and Tatsuya Ishikawa and Masanori Mitsugi and Yasunori Kitakoshi and Takahiro Tanaka and Naomi Simumba and Kentaro Tanaka and Hiroaki Wakabayashi and Masato Sampei and Michiaki Tatsubori},
booktitle = {ICASSP 2024},
year = {2024}
}