Classification of Natural and Artificial Forests from KOMPSAT-3/3A/5 Images Using Deep Neural Network

Won Kyung Baek, Yong Suk Lee, Sung Hwan Park, Hyung Sup Jung

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Satellite remote sensing approach can be actively used for forest monitoring. Especially, it is much meaningful to utilize Korea multi-purpose satellites, an independently operated satellite in Korea, for forest monitoring of Korea, Recently, several studies have been performed to exploit meaningful information from satellite remote sensed data via machine learning approaches. The forest information produced through machine learning approaches can be used to support the efficiency of traditional forest monitoring methods, such as in-situ survey or qualitative analysis of aerial image. The performance of machine learning approaches is greatly depending on the characteristics of study area and data. Thus, it is very important to survey the best model among the various machine learning models. In this study, the performance of deep neural network to classify artificial or natural forests was analyzed in Samcheok, Korea. As a result, the pixel accuracy was about 0.857. F1 scores for natural and artificial forests were about 0.917 and 0.433 respectively. The F1 score of artificial forest was low. However, we can find that the artificial and natural forest classification performance improvement of about 0.06 and 0.10 in F1 scores, compared to the results from single layered sigmoid artificial neural network. Based on these results, it is necessary to find a more appropriate model for the forest type classification by applying additional models based on a convolutional neural network.

Original languageEnglish
Pages (from-to)1965-1974
Number of pages10
JournalKorean Journal of Remote Sensing
Volume37
Issue number3-6
DOIs
StatePublished - 2021

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