Classification Of Coastal Area And Extraction Of Beach Bearing Capacity Information By Hyperspectral Data

Hyperspectral remote sensing data has the characteristic of combining both image data and spectral data into one data set, each pixel of a hyperspectral image an entire spectrum (consisting of numerous contiguous, narrow spectral bands) is present. Compared with conventional multi-spectral remote sensing data, hyperspectral remote sensing data can be more useful for the identification and classification of surface Materials. In this paper, the author classified the coastal area using hyperspectral remote sensing data, and further more, succeeded in extraction of sand beach surface information related to bearing capacity characteristic. This thesis consists of the following three parts.In the first part, the author firstly reviewed hyperspectral technology, include its basic conception, theories and its developing history, then introduced preprocess of hyperspectral data concerning atmospheric correction, geometric correction and its basic analysis techniques. Finally the write preprocessed the hyperspectral data used in this thesis.In the second part, mainly concern with coastal area classification techniques using hyperspectral data. Based on analysis of spectrum features of coastal surface materials and basic classification method, the author proposed a hybrid decision tree classification algorithm and classified the coastal area.In the third part, mainly study on sand beach surface characteristic of bearing capacity by hyperspectral remote sensing data. Based on analysis of field spectrum of sand beach and its surface characteristic, the author made a thematic map, which indicated sand beach surface characteristic of bearing capacity. Final, the author discussed hyperspectral and its applications detecting coastal zone.