S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Development and Comparative Analysis of Geospatial Feature Automation Extraction System in Open-source Environment

Dong Gook Lee, Ji Ho You, and Hyun Jik Lee

(Received February 24, 2021; Accepted May 14, 2021)

Keywords: open source, geospatial feature extraction system, development, classification accuracy

The aim of this study is to develop a system for geospatial feature extraction from images to be obtained from CAS500-1/2 satellites currently being developed by the Ministry of Land, Infrastructure, and Transport, Republic of Korea. The feasibility of automatic geospatial feature extraction is verified by applying the relative radiometric normalization technique to KOMPSAT-3A satellite images, which are expected to have similar specifications to CAS500 images. Furthermore, the developed system is compared with commercial software to verify its classification accuracy. In this study, two KOMPSAT-3A satellite images were collected and relative radiometric normalization was performed. Identical parameters and threshold values were applied to both the commercial software and the developed system to extract geospatial features by feature class and analyze the classification accuracy using an error matrix. Image segmentation and image classification were performed for grassland, ground, roads, buildings, and water bodies. The results indicated a classification accuracy of 90% or higher, which was set as the accuracy goal. The difference in the classification accuracy of the two systems was less than approximately 1%, implying comparable performances for both systems. Using the geospatial feature extraction system developed by us, it is expected that basic data will be generated for monitoring national territory such as forests and urban areas.

Corresponding author: Ji Ho You, Hyun Jik Lee

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