Support Vector Machines(SVM) is a potential hyperspectral remote sensing classification method because it is advantageous to deal with problems with high dimensions,small samples and uncertainty.

Then,taking an OMISⅠimage as an example, performs many tests to present and analyze the laws of this model applied in vegetation classification using hyperspectral remote sensing.

This paper focuses on the establishment of a nonlinear mixing spectral model and its application to the vegetation classification using hyperspectral remote sensing, and discusses the application of vegetation classification methods of hyperspectral images, the selection of training samples and unmixing of mixed pixels.

Status and prospect of hyperspectral remote sensing technique in rock and mineral identification;

The multi-directional projection pursuit and the feature extraction of hyperspectral remote sensing image;

Study on hyperspectral remote sensing retriveral models about winter wheat stripe rust severity;

Monitoring leaf nitrogen accumulation with hyper-spectral remote sensing in wheat;

Image fusion algorithm of the hyper-spectral remote sensing appling to the survey of land utilization based on a curvature-wave transformation

By way of hyper-spectral remote sensing,we have gained the reflex spectrums of water body containing suspended particles and calculated the reflectivity theoretically.

Feasibility of monitoring and estimating peach yield with hyper spectral remote sensing

The application of hyper spectral remote sensing technology in diagnosing of crop nutrition information is summarized in this paper.

Based on the analysis of the characters of hyper spectral remote sensing,form the basic research and the resources and environment monitoring of the marine and the coastal zone,the application of hyper spectral remote sensing in estimating chlorophyll,oil-spill,sea ice and typical elements in the coastal zone is presented in the paper.