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Hyperspectral image classification using NRS with different distance measurement techniques

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Abstract

For the HSI classification, the recently introduced nearest regularized subspace (NRS) classifier outperform the sparse representation-based classification (SRC) and collaborative representation-based classification (CRC). The NRS method has two major distance measurements parts to classify approximation of the testing simple properly. One is the residual between the approximation and the corresponding pixel, and the other is the elements in the Tikhonov matrix. The main contribution of this paper is to find the optimum distance measures for NRS to increase the performance results and minimize the running time compared to their previous versions and other existing methods. The experiments were conducted with four distance measures such as Manhattan distance (MD), Euclidean distance(ED), Chi-square (X2) and Cosine distance (CS). The different distance measures are implemented in residual and Tikhonov Matrix calculations. To sum up, sixteen (16) distance measurement combinations are tested on Four Datasets, such as Indian Pines, Pavia University, KSC and Center of Pavia. The experiments show higher accuracy and reduced time as compared to other methods, with NRS_X2-MD and NRS_MD-MD as the top two combinations.

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Authors and Affiliations

  1. Department of Computer & Software Technology, University of Swat, Swat, KP, 19130, Pakistan

    Sarwar Shah Khan & Muzammil Khan

  2. Department of Software Engineering, University of Sialkot, Sialkot, 51310, Pakistan

    Sarwar Shah Khan & Muzammil Khan

  3. City University of Science and Information Technology, Peshawar, 25000, Pakistan

    Shahab Haider

  4. Kaunas University of Technology, 51386, Kaunas, Lithuania

    Robertas Damaševičius

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  1. Sarwar Shah Khan
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  2. Muzammil Khan
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  3. Shahab Haider
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  4. Robertas Damaševičius
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Correspondence to Muzammil Khan.

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Khan, S.S., Khan, M., Haider, S. et al. Hyperspectral image classification using NRS with different distance measurement techniques. Multimed Tools Appl 81, 24869–24885 (2022). https://doi.org/10.1007/s11042-022-12263-x

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  • DOI: https://doi.org/10.1007/s11042-022-12263-x

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