Fuzzy C-Means Technique for Band Reduction and Segmentation of Hyperspectral Satellite Image

2021 ◽  
Vol 10 (4) ◽  
pp. 79-100
Author(s):  
Saravanakumar V. ◽  
Kavitha M. Saravanan ◽  
Balaram V. V. S. S. S. ◽  
Anantha Sivaprakasam S.
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Hyperspectral Remote Sensing ◽  
Fuzzy Algorithm ◽  
Fuzzy C Means ◽  
Segmentation Process ◽  
Remote Sensing Satellite

This paper put forward for the segmentation process on the hyperspectral remote sensing satellite scene. The prevailing algorithm, fuzzy c-means, is performed on this scene. Moreover, this algorithm is performed in both inter band as well as intra band clustering (i.e., band reduction and segmentation are performed by this algorithm). Furthermore, a band that has topmost variance is selected from every cluster. This structure diminishes these bands into three bands. This reduced band is de-correlated, and subsequently segmentation is carried out using this fuzzy algorithm.

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

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 435-439
Author(s):  
S. Jia ◽  
Q. Zhao ◽  
L. Wang ◽  
Y. Li
Keyword(s):  
Remote Sensing ◽  
Computational Cost ◽  
Random Projection ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Dimensional Image ◽  
Fuzzy C Means ◽  
Ensemble Algorithm ◽  
Low Dimensional ◽  
Hyperspectral Remote Sensing Image

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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