Combined Inversion of Hyper-Spectral Remote Sensing of Space and Spectrum for Lake Chlorophyll

Journal of Atmospheric and Environmental Optics ›› 2017, Vol. 12 ›› Issue (6): 428-434.

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Combined Inversion of Hyper-Spectral Remote Sensing of Space and Spectrum for Lake Chlorophyll

PAN Banglong1, SHEN Huiyan1, SHAO Hui2, LI Weihua1

( 1 School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China;
2 School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230601, China)

Received:2017-08-28 Revised:2017-09-18 Online:2017-11-28 Published:2017-11-13
Supported by:
Supported by Natural Science Foundation of Anhui Province(安徽省自然科学基金，1708085MD90), Natural Science Foundation of Anhui Colleges(安徽省高校自然科学基金，KJ2015A321,KJ2017A500,KJ2015JD07),Key Laboratory Fund of General Optical Calibration and Characterization Technology, Chinese Academy of Sciences(中国科学院通用光学定标与表征及时重点实验室基金, 2015GBZS012)

Abstract

Abstract:

Fine spectral information of hyper-spectral remote sensing provides a good prospect for parameters of water color inversion by remote sensing. However, for high spectral resolution but low spatial resolution, the current hyper-spectral remote sensing inversion models and algorithms of water color are generally lack of effective use of spatial information, so the model is often difficult to ensure accuracy and stability. Taking Chaohu Lake, Anhui, China, as the study area, based on the space eight neighborhoods and genetic algorithm, hyper-spectral remote sensing HSI data of HJ-1A satellite is combined with the measured sample data to construct a hyper-spectral remote sensing inversion model of chlorophyll by the in-depth analysis of the spectral characteristics of water body. Based on matlab7.0 platform, the parameters of inversion model is calculated by the combined spectral indices and genetic algorithm. Under the spatial neighborhood analysis and genetic iteration, the concentration of chlorophyll is solved. The results show that the genetic algorithm abandons the traditional search methods, optimizes and searches water color space randomly using simulated evolution in the vicinity of the spatial domain by spectral information, jumps out of the local extreme point, can effectively improve the accuracy of model inversion.

Key words: chlorophyll, inversion of remote sensing, spatial neighborhood, genetic algorithm

CLC Number:

TP791

PAN Bang-Long, SHEN Hui-Yan, SHAO Hui, LI Wei-Hua. Combined Inversion of Hyper-Spectral Remote Sensing of Space and Spectrum for Lake Chlorophyll[J]. Journal of Atmospheric and Environmental Optics, 2017, 12(6): 428-434.

References

[1] Chang C H, Liu C C, Wen C G. Integrating semi-analytical and genetic algorithms to retrieve the constituents of water bodies from remote sensing of ocean color [J]. Opt. Express, 2007, 15(2): 252-256.
[2] Ma Ronghua, Tang Junwu, Duan Hongtao, et al. Progress in lake water color remote sensing [J]. Journal of Lake Sciences, 2009, 21(2): 143-158(in Chinese).
马荣华, 唐军武, 段洪涛, 等. 湖泊水色遥感研究进展 [J]. 湖泊科学, 2009, 21(2): 143-158.
[3] Miller R L, McKee B A. Using MODIS Terra 250 m imagery to map concentrations of total suspended matter in coastal waters [J]. Remote Sens. Environ., 2004, 93(1): 259-266.
[4] Dekker A G, Vos R J, Peters S W M. Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data [J]. Int. J. Remote Sens., 2002, 23(1): 15-35.
[5] Zhan Haigang, Shi Ping, Chen Chuqun. A genetic algorithm for retrieval of water constituents from ocean color remote sensed data in case 2 waters [J]. Journal of Remote Sensing, 2004, 8(1): 31-36(in Chinese).
詹海刚, 施平, 陈楚群. 基于遗传算法的二类水体水色遥感反演 [J].遥感学报, 2004, 8(1): 31-36.
[6] Huang Jiacong, Wu Xiaodong, Gao Junfeng, et al. Cyanobacteria bloom prediction model and parameters optimization based on genetic algorithm [J]. Acta Ecologica Sinica, 2010, 30(4): 1003-1010(in Chinese).
黄佳聪, 吴晓东, 高俊峰, 等. 蓝藻水华预报模型及基于遗传算法的参数优化 [J]. 生态学报, 2010, 30(4): 1003-1010.
[7] Wang Jiahua, Han Donghai. Analysis of near infrared spectra of apple SSC by genetic algorithm optimization [J]. Spectroscopy and Spectral Analysis, 28(10): 2308-2311(in Chinese).
王加华, 韩东海. 基于遗传算法的苹果糖度近红外光谱分析 [J]. 光谱学与光谱分析, 2008, 28(10): 2308-2311.
[8] Hao Panchao. Semi-Supervised Graph Clustering with Spatial-Spectral Kernel and Its Application in Hyperspectral Image [D]. Chongqin: Master’s Thesis of University of Chongqing, 2014(in Chinese).
郝盼超. 面向高光谱图像的空谱核半监督图聚类算法研究 [D]. 重庆: 重庆大学硕士论文, 2016.
[9] Huang Taimin. A Study of Spectral-Spatial Based Hyperspectral Image Classification [D]. Xi’an: Master’s Thesis of Xidian University, 2014(in Chinese).
黄泰民. 基于空谱联合的高光谱图像分类研究 [D]. 西安: 西安电子科技大学硕士论文, 2014.
[10] Li Suju, Wu Qian, Wang Xuejun, et al. Correlations between reflectance spectra and contents of chlorophyll-a in Chaohu Lake [J]. Journal of Lake Sciences, 2002, 14(3): 228-230(in Chinese).　
李素菊, 吴倩, 王学军, 等. 巢湖浮游植物叶绿素含量与反射光谱特征的关系 [J]. 湖泊科学, 2002, 14(3): 228-230.
[11] Wu M, Zhang W, Wang X J. Application of MODIS satellite data in monitoring water quality parameters of Chaohu Lake in China [J]. Environ. Monit. Assess., 2009, 148(1): 255-265.

Combined Inversion of Hyper-Spectral Remote Sensing of Space and Spectrum for Lake Chlorophyll

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