文献类型：期刊文献

英文题名：Harbor Aquaculture Area Extraction Aided with an Integration-Enhanced Gradient Descent Algorithm

作者：Zhong, Yafeng[1];Liao, Siyuan[2];Yu, Guo[2];Fu, Dongyang[2,3];Huang, Haoen[2]

机构：[1]Guangdong Ocean Univ, Coll Chem & Environm Sci, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Guangdong Prov Engn & Technol Res Ctr Marine Remo, Zhanjiang 524088, Peoples R China

年份：2021

卷号：13

期号：22

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:000771958500009)、、EI(收录号：20214711203152)、Scopus(收录号：2-s2.0-85119373160)、WOS

基金：This research was funded by Key Projects of the Guangdong Education Department (2019KZDXM019); Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (ZJW-2019-08); High-Level Marine Discipline Team Project of Guangdong Ocean University (00202600-2009); First Class Discipline Construction Platform Project in 2019 of Guangdong Ocean University (231419026); Guangdong Graduate Academic Forum Project (230420003); Postgraduate Education Innovation Project of Guangdong Ocean University (202159); Provincial-Level College Student Innovation and Entrepreneurship Training Project (S202110566052).

语种：英文

外文关键词：integration-enhanced gradient descent algorithm; harbor aquaculture area extraction; GaoFen-1

外文摘要：In this study, the harbor aquaculture area tested is Zhanjiang coast, and for the remote sensing data, we use images from the GaoFen-1 satellite. In order to achieve a superior extraction performance, we propose the use of an integration-enhanced gradient descent (IEGD) algorithm. The key idea of this algorithm is to add an integration gradient term on the basis of the gradient descent (GD) algorithm to obtain high-precision extraction of the harbor aquaculture area. To evaluate the extraction performance of the proposed IEGD algorithm, comparative experiments were performed using three supervised classification methods: the neural network method, the support vector machine method, and the maximum likelihood method. From the results extracted, we found that the overall accuracy and F-score of the proposed IEGD algorithm for the overall performance were 0.9538 and 0.9541, meaning that the IEGD algorithm outperformed the three comparison algorithms. Both the visualized and quantitative results demonstrate the high precision of the proposed IEGD algorithm aided with the CEM scheme for the harbor aquaculture area extraction. These results confirm the effectiveness and practicality of the proposed IEGD algorithm in harbor aquaculture area extraction from GF-1 satellite data. Added to that, the proposed IEGD algorithm can improve the extraction accuracy of large-scale images and be employed for the extraction of various aquaculture areas. Given that the IEGD algorithm is a type of supervised classification algorithm, it relies heavily on the spectral feature information of the aquaculture object. For this reason, if the spectral feature information of the region of interest is not selected properly, the extraction performance of the overall aquaculture area will be extremely reduced.