Hyperspectral remotely-sensed data dimensionality reduction method based on improved hierarchical clustering

Abstract

The invention discloses a hyperspectral remotely-sensed data dimensionality reduction method based on improved hierarchical clustering. The hyperspectral remotely-sensed data dimensionality reduction method comprises the following steps of: selecting hyperspectral remotely-sensed image data to be analyzed, wherein the hyperspectral remotely-sensed image data comprise L wave bands; calculating a spectral distance between every two wave bands by a security identification (SID) algorithm to acquire a clustering center extracted for setting a spectral distance matrix and a number k of wave bands to be selected; performing clustering analysis on the image data by a hierarchical clustering method based on a similarity distance matrix; acquiring k clustering center data to finish a feature extraction process; and selecting the most representative wave band from each clustering center to acquire k wave bands to finish a feature selection process. By the method, the dimensionality reduction efficiency can be improved; and the data information loss caused by the conventional hyperspectral remotely-sensed data dimensionality reduction method is reduced.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral remote sensing image processing, and in particular relates to a hyperspectral remote sensing data dimensionality reduction method based on improved hierarchical clustering. Background technique [0002] Hyperspectral Remote Sensing (Hyperspectral Remote Sensing) refers to the technology of using many narrow electromagnetic wave bands to obtain data about objects. It is one of the major technological breakthroughs in earth observation in the last 20 years of the 20th century. Frontier technology of remote sensing during the year. Compared with conventional multispectral remote sensing, hyperspectral data has the characteristics of large data volume, many narrow bands, strong correlation between bands, more information redundancy, and map integration. However, it is precisely its massive data and high-dimensional features that bring great difficulties to the transmission and storage of hypers...

AI Technical Summary

Problems solved by technology

Due to different characteristics of different targets, their distance calculation methods should be different, and different initial distance calculation methods may lead to different results; 2) The number of clusters

The number k of clusters needs to be specified in advance. When the prior knowledge of the data cannot be obtained, the specific size of the value cannot be predicted; 3) Clustering data calculation problems

For massive hyperspectral remote sensing data, simply using all the data may lead to an extreme increase in computational complexity

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