A Kernel spectralanglemapper (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of the remote sensing image. The so-called KSAM algorithm is achieved by introducing the kernel meth...
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ISBN:
(纸本)9781479927630
A Kernel spectralanglemapper (KSAM) algorithm is proposed to deal better with the nonlinear classification problem of the remote sensing image. The so-called KSAM algorithm is achieved by introducing the kernel method into the standard spectralanglemapper (SAM) algorithm. Experimental results indicate that the classification accuracy of the KSAM algorithm is superior to one of the SAM algorithm in the remote sensing image classification. However the kernel parameters of the polynomial and sigmoid kernel functions of the algorithm are excessively sensitive. A narrow bound of the kernel parameters in the polynomial and sigmoid kernel functions can be chosen for the optimal classification of the remote sensing image. The classification performance of the Radial Basis Function (RBF) kernel function is superior to one of the polynomial and sigmoid kernel functions. A wide bound of the kernel parameter in the RBF kernel function can be chosen for the optimal classification of the remote sensing image in the KSAM algorithm.
Sublingual vein is one of the important features on tongue surface, which may have pathological relationship with some diseases. Extracting sublingual veins accurately is the primitive work of computer-aided tongue di...
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ISBN:
(纸本)9781424447138
Sublingual vein is one of the important features on tongue surface, which may have pathological relationship with some diseases. Extracting sublingual veins accurately is the primitive work of computer-aided tongue disease diagnosis. Most existing sublingual veins extraction methods are using sublingual images captured by traditional CCD cameras. However, these conversional methods impede the accurate analysis on the subjects of sublingual veins because of the limited information of the images. To solve these issues, a hyperspectral tongue imaging system instead of a digital camera is used to capture sublingual images. Then an improved spectralanglemapper (ISAM) algorithm for automatic sublingual veins extraction was presented. In this algorithm, the spectral of sublingual veins were extracted and the spectralangles of all bands and partial bands were calculated respectively. Finally, the sublingual veins were extracted according to the spectralangles. The experimental results demonstrate that this algorithm can extract the sublingual veins more accurately.
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which ...
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ISBN:
(纸本)9781479958368
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this paper, an improved ISODATA algorithm is proposed for hyperspectral images classification. The algorithm takes the maximum and minimum spectrum of the image into consideration and determines the initial cluster center by the stepped construction of spectrum accurately. The classification experiment results show that using the improved ISODATA algorithm can determine the initial cluster number adaptively. In comparison with the SAM (spectralanglemapper) algorithm and the original ISODATA algorithm, a better performance of the proposed ISODATA method is shown in the part of results.
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