Taking Xuzhou city as an example, the urban green space categories system are established using multi-temporal/-source
remotely sensed images. After classification adopted decision tree and object-oriented methods, the urban green space
pattern changes are captured and evolution rules are analyzed based on the landscape pattern indices on the patch/class
and landscape metrics. In addition, the economic/social statistics are listed for quantitative analyzing dynamic evolution.
Finally, the all driving factors impacting urban green space pattern are analyzed using the principal component analysis.
Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have
gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable
classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image
classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.
Noises are inevitable in Hyperspectral Remote Sensing (HRS) image, it is very important to design effective filter to reduce the impacts of noises and enhance image quality and information content. Based on the characteristics of HRS image, three filtering strategies, including image dimension filtering, spectral dimension filtering and three-dimensional filtering, are proposed in this paper. The principle of image dimension filtering is similar to traditional image filtering from spatial and frequency domain. The image of each band is viewed as an independent set and filtering operation is used to it. Some filters, including mean filter, medium filter and frequency filter, are used to reduce noises in every band. The key idea of spectral dimension filtering is to take every pixel as the processing target, and the gray value (or albedo) of the pixel on all bands will form a spectral vector. Filter is used to the spectral vector of every pixel, and mean filter with different scales is tested in this paper. Three-dimension filtering is different from the former two methods by its spatial and spectral dimension processing simultaneously. It views HRS image as a large data cube with row, column and layer (band), so filter is based on data cube. In this paper the 3×3×3 cube is used as filtering template, and that means those neighbors of adjacent bands of a pixel on a given band will be used to filter, so both spatial and spectral information is considered in this new method. Finally, some examples are experimented and quality assessment of sole band, similarity measure to some pixels and other statistical indexes are used to assess the performance, and then related conclusions and suggestions are given.
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