Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems...

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Bibliographic Details
Published in:arXiv.org 2023-03
Main Authors: Agersborg, Jørgen A, Luppino, Luigi T, Stian Normann Anfinsen, Jane Uhd Jepsen
Format: Article
Language:English
Subjects:
Algorithms
Change detection
Deep learning
Landsat 5
Landsat satellites
Machine learning
Mapping
Mortality
Radar imaging
Radarsat
Remote sensing
Satellite imagery
Synthetic aperture radar
Tundra
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Summary:Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites' respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality.
ISSN:2331-8422
DOI:10.48550/arxiv.2203.00049