SVM (Support Vector Machine) is a new kind of machine learning method , it can solve classification and regression
problems very successfully and accomplish classification with small sample incident perfectly. In this paper, the NPA is
proposed to compute the optimization problem to achieve the classification for hyperspectral remote sensing (RS) image
by "1 VS m" strategy and radial basis kernel function. Besides, a new method, the dual-binary tree + SVM algorithm is
proposed, to solve the mutil-class, high-dimensional(HD) problems of hyperspectral RS image. In the end, the test is
carried on the OMIS image. The comparative results of this algorithm with other methods are given, which shows that
our algorithm is very competitive, particularly for the small samples and non-equilibrium surface features. Both the
accuracy and speed of classification are improved greatly.
KEYWORDS: Remote sensing, Image classification, Bismuth, Information theory, Absorption, Landsat, Data processing, Lithium, Current controlled current source, Earth observing sensors
In the traditional BNC model, the relationship between the attributes are the same for all the instances of the class
variable C. BMN classifier is a generalized form of BNC, in the sense that it allows different relationships among
attributes for every values of the class variable, and provides a unique net structure for every object class. This paper
proposes Bayesian Multi-nets (BMN) Models based on the analysis of conditional mutual information(CMI) between
image features of different objects classes, and constructs BMN classifier for remote sensing images on the basis of
experiment. Classification accuracy of single objects in BMN classifier outperforms that of traditional BN, proves the
latent value of the proposed models in the classification of remote sensing images.
In this paper, we set forth the principle of Cosine Backscatter Model. In the model, and a new algorithm that doesn't omit azimuth angle and can extract DEM in mountainous area was introduced. First, the Radar image is divided into several regions by edge information using Lapalce algorithm. In one region, the image gray level changes slowly. Second, in the same region, we could assume that slope changes slowly, azimuth angle and range angle are affected by their neighbor pixels, the image gray level of pixel is changed by its neighbor pixels, azimuth angle and range angle were assessed from a seed. From known point, we get azimuth angle and range angle respectively by derivative; balance the value through iterative computation by ratio data and Cosine Backscatter Model. In neighbor regions, we get seed of gradient angle by average gray level of two regions and give amend index. From this point, we can get other point gradient angle same as the second step. Then we extract DEM in all regions. By applying this model, the DEM of Zhangbei of Hebei province were assessed. Through checking against the topographic map, the DEM error is little.
The edges in an image could be considered as boundary lines between the classes to be analyzed and distinguished. Determining those boundary lines is important for the detection of image edges. As to the edges in the super dimensional spectral image data, the lower density zone in spectral space could be considered as the seeking range for the boundary between different objects. This paper discusses the principles and methods of density analysis for super dimensional spectral image data. One key for that is to determine the statistical unit of super dimensional space. The approaches include the method according the gray level combination in spectral space, the method of statistics starting from first pixel of image, the method of taking as the reference the first component of principal component transformation for spectral space, the method of determining the unit super sphere based on sample set etc. The experiments using one of the methods have shown the effectiveness of spectral space density analysis and have been discussed.
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