Technology Of Separating Remotely Sensed Weak Information About Heavy Metal Pollution In Soil Based On ICA And Software Prototype

Soil is the basis for agricultural production and is necessary living environment of crop plants. However, with rapid development of modern large-scale industrial and agricultural production and continuous urbanization process, a large number of toxic, harmful substances enter the soil and seriously damage the soil's ability of self adjustment, leading to soil pollution, destruction, degradation and destroy the benign circle of agricultural ecosystem and living environment of human. Therefore, rapid, accurate, effective and dynamic detection of soil pollution situation can provide scientific basis for soil pollution governance.Under the auspice of National Natural Science Foundation of China, the paper researchs heavy metal distribution in soil and crop spectrum features in rice fields of Changchun City's suburb. With remotely sensed biogeochemical method and modern spectrum analysis method, expecially independent component analysis method, the paper processes spectrum data and extracts crop spectrum features in contaminated areas. The established soil pollution estimation model offers refference for assessing soil heavy metal pollution degree. Specific studies are as follows:The pretreatment algorithms to hyperspectrum data is programmed in matlab language. The simulation results show that PCA not only can remove noise effectively but also can reduce dimensionality when as much as information is retained, FastICA algorithm separate spectrum features effectively.Based on pretreated hyperspectrum data, derivative spectrum and continuum normalized spectrum, 39 spectrum feature parameters are extracted. Results of analyzing rice spectrum features show that changes of rice spectrum reflectance are significant in different growth period and under special environment. These extracted parameters have strong relationship with soil heavy metal pollution.The results of single-factor regression analysis on leaf chlorophyll content and spectrum features show that linear correlation coefficients are different, correlation coefficient of position of red edge and chlorophyll content is most large and this shows that position of red edge can better reflect the chlorophyll content. Single-factor regression analysis method provides support for feature information extraction of rice pollution stress.The software prototype of separating weak information about soil pollution is done by the tools including Matlab, ASD View Spectro and Excel 2003.