SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation

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. Fo...

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Bibliographic Details
Published in:	ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3865 - 3869
Main Authors:	Kimura, Daiki, Ishikawa, Tatsuya, Mitsugi, Masanori, Kitakoshi, Yasunori, Tanaka, Takahiro, Simumba, Naomi, Tanaka, Kentaro, Wakabayashi, Hiroaki, Sampei, Masato, Tatsubori, Michiaki
Format:	Conference Proceeding
Language:	English
Published:	IEEE 14-04-2024
Subjects:	Geospatial Geospatial analysis Image translation Pre-training Radar polarimetry Signal processing Speech processing Task analysis Training Vegetation mapping
Online Access:	Get full text
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Summary:	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.
ISSN:	2379-190X
DOI:	10.1109/ICASSP48485.2024.10448409