The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 435–439, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-435-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 435–439, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-435-2020

  21 Aug 2020

21 Aug 2020

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

S. Jia, Q. Zhao, L. Wang, and Y. Li S. Jia et al.
  • School of Geomatics, Liaoning Technical University, Fuxin Liaoning 123000, China

Keywords: Dimensionality reduction, Random Projection, Bias-Corrected FCM algorithm, Hyperspectral remote sensing, 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.