Hyperspectral Data Mining Supported By Temporal And Spatial Information

From the beginning of remote sensing, imaging technology has advanced in two major ways: one is the improvement in the spatial resolution of images, another is the improvement in the spectral resolution of images. Conventional multispectral scanners record up to 10, or so, spectral bands with bandwidths on the order of 0.10um in visible to short wave infrared bands. Furthermore, hyperspectral imaging, or called imaging spectrometry, can acquire images in hundreds of registered, contiguous spectral bands such that for each picture element it is possible to derive a complete reflectance spectrum.Hyperspectral remote sensing effectively make the spectral feature and geometric characters of objects together. From the view of earth observation from space, hyperspectral data provide human being more abundant information, not only in the deep explorations of object's physical and chemical characters, but also in the precise classification of different objects and knowledge innovation. In case of so much spectral bands and such huge quantities of data, some conventional data processing methods can not play good roles. Aiming at the hyperspectral image cube, the understanding and data processing in image spatial dimension must be changed to that completed in the spectral dimension. This dissertation is just concentrated on above aspects and evolved in the systematic and innovative views.This dissertation begin from the introduction on hyperspectral remote sensing technology. In the second and third chapters, two key points in hyperapectral data processing and analysis area, hyperspectral data calibration and parameterizationand, and hyperspectral image classification and identification, were dissertated. The fourth and fifth chapter pays more attentions to the hyperspectral data mining supported by the temporal and spatial information. In general, this study have some advantages as follows:(1) As for spectral feature selection, spectral bands selection and objects quickly finding in image cube were provided. On the other hand of spectral feature extraction, several selections of spectral parameterization were also provided. Considering the hyperspectral geological remote sensing, stratum spectralhistogram was established specially for 14 strata in Tulufan anticline.(2) After discussion on the traditional image classification, a new method, Expert Decision Classification Based on Feature Optimization, was provided here. It is designed out in accord with two principles: one is the spectral feature optimization and parameterization, another is fuzzy and expert decision in pixel identification. Comparing with other method, this method can acquire more accurate classification results.(3) Several spectra of man-made camouflage materials were provided here. In the SWIR, the position and relative intensities of the major absorption features associated with water are difficult to duplicate due to the complex architecture of vegetation. In addition, convex geometry projection was successfully used in the different metal material detection.(4) On the bases of vegetation spectral analysis and hyperspectral vegetation indices, Multi-temporal Indices Image Cube was put forward and used in the dynamic growing analysis of Japanese lettuce, Chinese cabbage, and wheat stressed by nitrogen or water contents.(5) In the area of hyperspectral data analysis supported by spatial information, Four application aspects were provided: spatial fusion based spectral reversion, hyperspectral data analysis associated with pixel position analysis, spectral unmixing and classification in the field patch units, and image classification supported by digital geomorphology model.