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引用本文:黄帅,宋开宏,罗菊花,赵晋陵,马荣华.基于梯度变换的浅水湖泊围网区遥感提取算法.湖泊科学,2017,29(2):490-497. DOI:10.18307/2017.0225
HUANG Shuai,SONG Kaihong,LUO Juhua,ZHAO Jinling,MA Ronghua.A remote sensing extraction algorithm of enclosure culture area in shallow lakes based on gradient transform. J. Lake Sci.2017,29(2):490-497. DOI:10.18307/2017.0225
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基于梯度变换的浅水湖泊围网区遥感提取算法
黄帅1, 宋开宏1, 罗菊花2, 赵晋陵1, 马荣华2
1.安徽大学计算智能与信号处理教育部重点实验室, 合肥 230039;2.中国科学院流域地理学重点实验室中国科学院南京地理与湖泊研究所, 南京 210008
摘要:
获取并掌握浅水湖泊围网养殖区域的时空分布信息对合理规划围网养殖进而提升湖泊水质具有重要意义.本文以长江下游典型的围网养殖浅水湖泊——阳澄湖作为研究区,利用资源三号(ZY-3)高分遥感影像,针对围网区与非围网区的光谱空间变化特征,采用梯度变换方法,尝试提出一种浅水湖泊围网区的遥感提取算法;并以人工解译结果作为参考,对提取结果进行验证.研究结果发现该算法对浅水湖泊围网养殖区的提取精度为90.66%,可进一步用于开展长时序的浅水湖泊围网区动态变化研究,进而为湖泊环境的政府部门制定湖泊水质提升和围网区合理规划政策提供决策依据.
关键词:  浅水湖泊  围网养殖区  梯度变换  卫星遥感  阳澄湖
DOI:10.18307/2017.0225
分类号:
基金项目:国家自然科学基金项目(41301375)和苏州市阳澄湖生态系统优化提升研究基金项目(SZLHZ2014-G-003)联合资助.
A remote sensing extraction algorithm of enclosure culture area in shallow lakes based on gradient transform
HUANG Shuai1, SONG Kaihong1, LUO Juhua2, ZHAO Jinling1, MA Ronghua2
1.Key Laboratory of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, P. R. China;2.Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, P. R. China
Abstract:
Mastering the tempo-spatial distribution information of enclosure culture areas is useful for a scientific planning of enclosure culture areas of shallow lakes and making effective measurements to improve water quality. This study took Lake Yangcheng as a study area, which is a typical enclosure culture area in the lower reaches of Yangtze River. An extraction algorithm of remote sensing images is proposed through the gradient transformation of remote sensing data based on the differences between enclosure and non-enclosure areas in spectral space. A high-resolution ZY-3 image was used to acquire the spatial distribution of enclosure culture areas in shallow lakes. The enclosure culture areas extracted using the proposed extraction algorithm and the manual visual interpretation were compared to evaluate the classification accuracy. The results show that the overall classification accuracy reached to 90.66%. The proposed method could be used to monitor the dynamic changes of enclosure culture areas in shallow lakes based on the ZY-3 images.
Key words:  Shallow lakes  enclosure culture area  gradient transformation  satellite remote sensing  Lake Yangcheng
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