干旱区生态环境知识资源中心(Arid): Change detection over the Aral Sea using relative radiometric normalization based on deep learning

Arid

DOI	10.1080/2150704X.2023.2242589
	Change detection over the Aral Sea using relative radiometric normalization based on deep learning
	Kim, Taeheon; Yun, Yerin; Park, Seonyoung; Oh, Jaehong; Han, Youkyung
通讯作者	Han, Y
来源期刊	REMOTE SENSING LETTERS
ISSN	2150-704X
EISSN	2150-7058
出版年	2023
卷号	14 期号:8 页码:821-832
英文摘要	The desertification of the Aral Sea causes various environmental destruction and local community collapse. In order to prepare countermeasures, changed areas caused by desertification should be quickly and accurately detected. However, if the radiometric dissimilarity between bi-temporal satellite images is severe, the probability of false detection increases. Therefore, a relative radiometric normalization (RRN) approach based on deep learning is proposed to accurately detect the changed areas. .To this end, a deep learning network is designed to extract pseudo-invariant features (PIFs), which is invariant pixels with similar spectral characteristics. More specifically, training dataset generated based on the center points of objects defined by applying an image segmentation are inputted to the network. After training the deep learning network, the PIFs are extracted by measuring the similarity between deep features. The radiometric dissimilarity is non-linearly normalized by estimating an artificial neural network based on the extracted PIFs. Then, changed areas by desertification are detected in object units by combining the pixel-based change map and the segmented objects. Bi-temporal Landsat-8 images acquired from the Aral Sea in 2013 and 2021 were used as experimental images. The proposed method showed sufficient performance for detecting the overall change in land cover due to desertification.
英文关键词	Aral sea relative radiometric normalization deep learning pseudo-invariant feature
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001053388500001
WOS关键词	REGRESSION
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398354
推荐引用方式 GB/T 7714	Kim, Taeheon,Yun, Yerin,Park, Seonyoung,et al. Change detection over the Aral Sea using relative radiometric normalization based on deep learning[J],2023,14(8):821-832.
APA	Kim, Taeheon,Yun, Yerin,Park, Seonyoung,Oh, Jaehong,&Han, Youkyung.(2023).Change detection over the Aral Sea using relative radiometric normalization based on deep learning.REMOTE SENSING LETTERS,14(8),821-832.
MLA	Kim, Taeheon,et al."Change detection over the Aral Sea using relative radiometric normalization based on deep learning".REMOTE SENSING LETTERS 14.8(2023):821-832.