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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.19 No.4
- 발행연도
- 2019.12
- 수록면
- 272 - 282 (11page)
- DOI
- 10.5391/IJFIS.2019.19.4.272
이용수
초록· 키워드
오류제보하기
The objective of this study is to develop a classification method based on convolutional neural network (CNN) and Sentinel-2 satellite imagery including the spectral feature, spectral index and spatial feature together as an input to answer forest monitoring problem. This research also used contextual information on Indonesia National Standard Agency’s document for Land cover classification as a baseline for feature extraction to get the appropriate classifier feature. The test set was located in Semarang, Central Java, Indonesia. The research workflow consists of defining forest class based on Indonesia National Standard Agency for Land cover classification, extracting optical image features based on contextual information of the forest class definition, extracting image features from the Sentinel-2 satellite image, and classifying image object features using CNN classifier. Image segmentation produced 1,211 segments/objects by using eCognition software. Subsequently, these objects were used as a dataset. Overall accuracy was used to evaluate the performance of the classification result. The result showed the classification method results in this study yielded high overall accuracy (97.66%) when using CNN with the image features like NDVI, Brightness, GLCM homogeneity and Rectangular fit. Small improvement of overall accuracy was also achieved when it was compared to GBT with an overall accuracy of 95.50%.
목차
Abstract
1. Introduction
2. Related Work
3. Experimental Method
4. Experimental Results
5. Conclusion
References
참고문헌 (22)
참고문헌 신청
N. Puletti / 2018 / Use of Sentinel-2 for forest classification in Mediterranean environments / Annals of Silvicultural Research 42(1):32~38
Qian Weng / 2018 / Land-use scene classification based on a CNN using a constrained extreme learning machine / International Journal of Remote Sensing 39(19):6281~6299
T. Zhang / 1990 / Band selection in Sentinel-2 satellite for agriculture applications / Proceedings of 2017 23rd International Conference on Automation and Computing (ICAC) :1~6
E. J. Rugel / 2017 / Beyond the Normalized Difference Vegetation Index(NDVI) : developing a natural space index for population-level health research / Environmental Research 159:474~483
I. Goodfellow / 2016 / Deep Learning / MIT Press
Qian Weng / 2018 / Land-use scene classification based on a CNN using a constrained extreme learning machine / International Journal of Remote Sensing 39(19):6281~6299
T. Zhang / 1990 / Band selection in Sentinel-2 satellite for agriculture applications / Proceedings of 2017 23rd International Conference on Automation and Computing (ICAC) :1~6
E. J. Rugel / 2017 / Beyond the Normalized Difference Vegetation Index(NDVI) : developing a natural space index for population-level health research / Environmental Research 159:474~483
I. Goodfellow / 2016 / Deep Learning / MIT Press
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