Hyperspectral Image Dimensionality Reduction And Segmentation Research

The characteristics of hyperspectral remote sensing data are numerous channels, high spectral resolution, and large amounts of data, which makes It easy to discriminate objects in the scene, however, the vast amounts of data not only makes it difficult to transport and store them, but also process them. Therefore, it is very important to reduce the dimension in the hyperspectral image analysis.Dimensionality reduction can generally fall into feature extraction and band selection. As a multianalysis tool, Principle Component Analysis (PCA) is commonly used in the feature extraction. However, such dimensionality reduction changes the physical meanings of the original bands, which makes it difficult to interpret the hyperspectral image. In this paper, in order to overcome the shortcoming we carry on a deep investigation into PCA, and propose four band selection algorithms which select a subsets from the original bands, in the end, we apply these algorithms to the hyperspectral segmentation.1. Proposed a Band selection algorithm based on feature weighting. In the PCA, the principle components are weight sum of the original bands, the elements of the transform metric are the weight, a certain combination of the elements of the metric can reflect the importance of certain band which can be used to select the bands, the results of the experiments indicate that the algorithm is sample and feasible.2. Proposed a Band Selection algorithm based on contribution to the principle components. With eigenvalues and eigenvectors of the covariance metrics of the original data, the contribution of a given band to a certain principle component can be calculated, the sum of the contribution of a given band to all important principle components can reflect the information of the band, which can be used to select the bands, the results of the experiments indicate that the algorithm is validate and needs little computation.3. Proposed a Band Selection algorithm based on Segmented PCA. In the previous two band selection algorithms, two bands with high correlation coefficient may be both selected, and because of the globality of the transform, some bands which are important in the local may be unselected, a Segmented PCA can overcome these shortcomings.4. Proposed a Band Selection algorithm based on a hybrid Wavelet-PCA. PCA is sufficient for reducing data volume, however it fails to preserve the spectral and local characteristic of the original data, Unlike PCA, wavelet decomposition which can preserve the spectral and local characteristic focuses on reducing each individual pixel in the spectral domain, this algorithm makes full use of the advantages of PCA and wavelet decomposition, which first performs an initial reduction using a wavelet decomposition, and then the band selection is applied. The results of the experiments indicate the algorithm can reduce the data into a lower dimensionality compared to the previous three algorithms.5. The segmentation of hyperspectral imagery is a key step in hyperspectral image understanding, target tracking, target recognition. In this paper, we summarized someunsupervised segmentation methods that were commonly used of hyperspectral image. In the experiments of segmentation, band selection algorithms proposed in this paper are used to reduce the dimensionality of the hyperspectral image.