In urban remote sensing, hyperspectral data is a potent supplement to traditional data sources ,such as aerial photograph and multispectral data. By using the detailed spectral information contained in hyperspectral data, fine classification of urban and man-made objects can be achieved through spectral matching techniques. Moreover, information for urban planning, urban change, environment monitoring and assessment, and corresponding social and economical information can be extracted. The aim of this dissertation is to develop methods and models for hyperspectral data processing and classification, and to use hyperspectral data for the recognition and classification of urban and man-made objects. This dissertation concentrate on the following aspects,
1. The basic principles and models for hyperspectral imaging systems, the methods and algorithms for hyperspetral data processing were studied. First, base upon the sensor and optical models the problems on sensor calibration, radiometric and atmospheric correction were studied. The various methods for reflectance conversion methods were discussed and further reduced into three kinds, the methods based upon radiative transfer theory , the normalization methods and empirical methods. The spectral matching concept were introduced. Several spectral library and tools for spectral analysis were presented. By using the empirical line method, reflectance conversion of urban hyperspectral data were made with high precision.
2. The spectral representation of urban and man-made objects in hyperspectral data were analyzed. First, the optical spectral characteristics of urban and man-made objects were studied by using spectra measured on the ground. Second, the spectral information represented in hyperspectral data were studied. Third , the comparison between high spectral resolution data and multispectral data were made. These studies indicate that the diversity and complex urban land cover types can be distinguished by using hyperspectral data.
3. A method for spectral feature extraction of urban and man-made objects was developed. Feature extraction is necessary for hyperspectral data processing because of the correlation and dispersion of discriminating information between and among the numerous spectral channels. The importance of the first and second order spectral statistical characteristics for classification were discussed. Improved CA transformation, a new feature extraction method, is developed, which is based upon the relative distance between and within class pairs. By using Bhattacharyya distance and scatter plot, the effectiveness of the feature extraction method was evaluated.
4. A detection and classification method, subspace projection and matched filtering were developed for hyperspectral data analysis. Pixel spectral can be considered as signal series, therefore, theory from signal detection can be introduced into hyperspectral data processing and classification. A “Spatially invariant, linearly additive” data model was introduced to discribe hyperspectral data. By using SD filter, orthogonal subspace projection technique and matched filters, the desired objects can be detected and further classified. Urban and man-made objects were classified using this method. The results make it clear that this method is rather efficient for hyperspectral data classification.
5. A strategy for detection and classification of urban and man-made objects were developed based on the spectral characteristics. This method classify the various land cover types hierarchically. As a testimony of the methods provided, a PHI and a HyMap hyperspectral images were experimented.