Manifold dimension reduction method of hyperspectral images based on image block distance

A hyperspectral image and image block technology, applied in the field of remote sensing image processing, can solve the problem of high computational complexity and achieve the effect of maintaining the local spatial structure

Inactive Publication Date: 2013-01-30
FUDAN UNIV
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It guarantees the robustness and global optimality of dimensiona

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  • Manifold dimension reduction method of hyperspectral images based on image block distance
  • Manifold dimension reduction method of hyperspectral images based on image block distance
  • Manifold dimension reduction method of hyperspectral images based on image block distance

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[0064] Below, the specific embodiment of the present invention is illustrated with simulation data and actual remote sensing image data respectively:

[0065] We compare four improved algorithms, IPAD-LLE, IPED-LLE, IPAD-ISOMAP and IPED-ISOMAP, with PCA and the original LLE, ISOMAP algorithms. They are common algorithms with better performance applied to hyperspectral data dimensionality reduction. In order to compare the performance of different dimensionality reduction algorithms, on the basis of dimensionality reduction, we use classification algorithms to perform classification operations on dimensionality reduction results, and evaluate the seven algorithms by analyzing the accuracy of classification. The classification algorithms used are K-Nearst Neighborhood (KNN) [9] and Support Vector Machine (SVM) [10]. The indicator for evaluating the classification results is the overall classification accuracy (Overall Accuracy, OA).

[0066] The Indiana Pine dataset used is th...

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Abstract

The invention belongs to the technical field of remote sensing image processing and in particular relates to a manifold dimension reduction method of hyperspectral images based on image block distance. According to the method, a novel distance measure, namely an image block distance measure is provided and is applied to neighborhood selection and lower dimension coordinate embedding in manifold learning, and a nonlinear dimension reduction method of novel hyperspectral remote sensing images is obtained. The physical properties of the hyperspectral images are utilized, the spectral information and space information of the images are combined, the local characteristics between data points can be better kept, and the characteristics of an original data set are well kept on the basis of reducing the image information redundancy to the greatest degree. The method has high applicability on various different hyperspectral data, and has important application values in detection and identification aspects of high-precision terrain classification and ground targets based on the hyperspectral remote sensing images.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a nonlinear dimensionality reduction method for hyperspectral remote sensing images. Background technique [0002] Remote sensing is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology with spectral technology. The hyperspectral imager simultaneously detects the two-dimensional geometric space and one-dimensional spectral information of the target on dozens to hundreds of very narrow and continuous spectral segments of the electromagnetic spectrum. In the hyperspectral image, each observation pixel can extract a complete a...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 普晗晔王斌张立明
Owner FUDAN UNIV
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