ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

With the increase of non-point source pollution emissions, the degree of eutrophication in water source is becoming seriously increase. Turbidity is an important parameter of water quality assessment, as an indicator of eutrophication. A discrete binary particle swarm optimization-partial least squares (PLS) model was proposed to reduce modeling uncertainty of turbidity retrieval using PLS and improve retrieval accuracy. A discrete binary particle swarm optimization was used to select original spectral reflectance and normalized spectral reflectance of concurrent HJ-1A HSI hyperspectral data with the turbidity obtained from October, 2015 in Weishan Lake as the input of partial least squares model. OSR-PLS and NSR-PLS model retrieving turbidity were developed by using original spectral reflectance and normalized spectral reflectance in full spectrum (HJ-1A HSI B26-B105 with 518nm to 870nm (central wavelength)). Meanwhile, the OSR-DBPSO-PLS and NSR-DBPSO-PLS models were developed to retrieve turbidity by using the selected original spectral reflectance and normalized spectral reflectance. The influence of spectral normalized and the characteristic band optimized on PLS model retrieval accuracy were analyzed based on the four models’ elevation. Finally, the highest accuracy model was used to retrieve the turbidity distribution in Weishan Lake. The results indicated that the accuracy of NSR-PLS (R2=0.91) model was better than that of OSR-PLS model (R2=0.50). The normalization of reflectance can improve PLS accuracy of turbidity retrieval. DBPSO can identify the optimal original and normalize spectral reflectance. The number of bands required for turbidity PLS modelling was reduced from 80 to 44 (OSR) and 36 (NSR), respectively. The OSR-DBPSO-PLS (R2=0.96) and NSR-DBPSO-PLS (R2=0.97) modelling based on 44 OSR and 36 NSR, respectively, had high accuracies, which were significantly higher than the OSR-PLS and NSR-PLS modelling by full spectrum. The NSR-DBPSO-PLS model with minimal comprehensive error was selected to retrieve turbidity in Weishan Lake, which was suitable for inland water turbidity retrieval based on HJ-1A HSI data.