利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱...利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。展开更多
This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vege...This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.展开更多
In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and entropy coding of image compression directly on the focal plane. The design is based on predictive coding for image deco...In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and entropy coding of image compression directly on the focal plane. The design is based on predictive coding for image decorrelation. The predictions are performed in analog domain by 2×2 pixel units. Both the prediction residuals and original pixel values are quantized and encoded in parallel. Since the residuals have a peak distribution around zero,the output codewords can be replaced by the valid part of the residuals' binary mode. The compressed bit stream is accessible directly at the output of CIS without extra disposition. Simulation results show that the proposed approach achieves a compression rate of 2. 2 and PSNR of 51 on different test images.展开更多
文摘利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。
基金Under the auspices of National Natural Science Foundation of China(No.41001363)
文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.
基金Supported by the National Natural Science Foundation of China(No.61036004)Tianjin Research Program of Application Foundation and Advanced Technology(No.13JCQNJC00600)
文摘In this paper, a CMOS image sensor(CIS) is proposed, which can accomplish both decorrelation and entropy coding of image compression directly on the focal plane. The design is based on predictive coding for image decorrelation. The predictions are performed in analog domain by 2×2 pixel units. Both the prediction residuals and original pixel values are quantized and encoded in parallel. Since the residuals have a peak distribution around zero,the output codewords can be replaced by the valid part of the residuals' binary mode. The compressed bit stream is accessible directly at the output of CIS without extra disposition. Simulation results show that the proposed approach achieves a compression rate of 2. 2 and PSNR of 51 on different test images.
