RANDOM PROJECTION BASED BIAS-CORRECTED FUZZY C-MEANS ALGORITHM FOR HYPERSPECTRAL REMOTE SENSING IMAGE SEGMENTATION

Abstract. To address the issue of the information redundancy for hyperspectral remote sensing image, this paper presents a novel ensemble algorithm that merges Random Projection (RP) and Bias-corrected Fuzzy C-means (BCFCM) algorithm. Since RP matrix has the abilities of preserving information nicely, it can be used to reduce the dimension of the image. To make full advantage of neighborhood relationship, BCFCM algorithm is improved to segment the low-dimensional image, in which Euclidean distances are retained to define the similarity between hyperspectral remote sensing image and the low-dimensional image. Finally, BCFCM algorithm is used to segment the fuzzy membership matrix of the ensemble algorithm. The proposed algorithm is evaluated by real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral remote sensing images. Segmentation performance is estimated by kappa coefficient and overall accuracy. Experimental results demonstrate that the proposed algorithm can achieve higher segmentation accuracy at a lower computational cost than that from conventional algorithms.

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Hyperspectral remote sensing image classification based on tighter random projection with minimal intra-class variance algorithm

Pattern Recognition ◽

10.1016/j.patcog.2020.107635 ◽

2021 ◽

Vol 111 ◽

pp. 107635

Author(s):

Quanhua Zhao ◽

Shuhan Jia ◽

Yu Li

Keyword(s):

Remote Sensing ◽

Image Classification ◽

Random Projection ◽

Remote Sensing Image ◽

Hyperspectral Remote Sensing ◽

Remote Sensing Image Classification ◽

Hyperspectral Remote Sensing Image

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Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w4-159-2015 ◽

2015 ◽

Vol XL-7/W4 ◽

pp. 159-167 ◽

Cited By ~ 5

Author(s):

S. Wang ◽

C. Wang

Keyword(s):

Remote Sensing ◽

Factor Analysis ◽

Reduction Method ◽

Hyperspectral Image ◽

Image Data ◽

Remote Sensing Image ◽

Hyperspectral Remote Sensing ◽

Hyperspectral Image Data ◽

Low Dimensional ◽

Hyperspectral Remote Sensing Image

Over the past thirty years, the hyperspectral remote sensing technology is attracted more and more attentions by the researchers. The dimension reduction technology for hyperspectral remote sensing image data is one of the hotspots in current research of hyperspectral remote sensing. In order to solve the problems of nonlinearity, the high dimensions and the redundancy of the bands that exist in the hyperspectral data, this paper proposes a dimension reduction method for hyperspectral remote sensing image data based on the global mixture coordination factor analysis. In the first place, a linear low dimensional manifold is obtained from the nonlinear and high dimensional hyperspectral image data by mixture factor analysis method. In the second place, the parameters of linear low dimensional manifold are estimated by the EM algorithm of find a local maximum of the data log-likelihood. In the third place, the manifold is aligned to a global parameterization by the global coordinated factor analysis model and then the lowdimension image data of hyperspectral image data is obtained at last. Through the comparison of different dimensionality reduction method and different classification method for the low-dimensional data, the result illuminates the proposed method can retain maximum spectral information in hyperspectral image data and can eliminate the redundant among bands.

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