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Proceedings Paper

Detection of forest disaster using satellite images with semantic segmentation
Author(s): Seong Wook Park; Yang Won Lee
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Paper Abstract

In recent 10 years, forest damage caused by forest fires in Korea has increased significantly compared to previous years. Therefore, interest and concern about damage caused by forest fires are very important in terms of environmental and ecosystem. According to various domestic and international research results, forests perform functions such as reporting of life resources, prevention of desertification, and adjustment of micro climate. There are many studies to extract the damage areas based on hyper spectral aerial image, high resolution satellite image, vegetation index and factors affecting the forest environment. However, there are limitations that the indexes have different threshold values depending on the region and season, and the threshold value must be continuously adjusted in order to detect the concentration of the damage areas. In this study, we detected forest disaster damaged areas through satellite image data and deep learning. We collected image data on Landsat satellite and applied to the detection of damaged area using U-net [1] and SegNet [2] models. We tried to verify the applicability of semantic segmentation for remote sensing, compare and evaluate each model, and build an optimal forest disaster detection model.

Paper Details

Date Published: 7 October 2019
PDF: 7 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551R (7 October 2019);
Show Author Affiliations
Seong Wook Park, Pukyong National Univ. (Korea, Republic of)
Yang Won Lee, Pukyong National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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