Journal of ISMAC
IRO Journals
Volume 3 — Issue 2 — June 2021

Hyperspectral Image Processing in Internet of Things model using Clustering Algorithm

https://doi.org/10.36548/jismac.2021.2.008
Pages: 163-175
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Bindhu V
Professor and Head, Department of Electronics and Communication Engineering, PPG Institute of Technology
G. Ranganathan
Professor and Head, Department of Electronics and Communication Engineering, Gnanamani College of Technology
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How to Cite

V, Bindhu, and G. Ranganathan. 2021. “Hyperspectral Image Processing in Internet of Things Model Using Clustering Algorithm”. Journal of ISMAC 3 (2): 163-75. https://doi.org/10.36548/jismac.2021.2.008.
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Keywords

— Hyperspectral Images
— Internet of Things
— Subspace Clustering
— Artificial Intelligence
— Sorting algorithm
Published: 21-06-2021

Abstract

With the advent of technology, several domains have b on Internet of Things (IoT). The hyper spectral sensors present in earth observation system sends hyper spectral images (HSIs) to the cloud for further processing. Artificial intelligence (AI) models are used to analyse data in edge servers, resulting in a faster response time and reduced cost. Hyperspectral images and other high-dimensional image data may be analysed by using a core AI model called subspace clustering. The existing subspace clustering algorithms are easily affected by noise since they are constructed based on a single model. The representation coefficient matrix connectivity and sparsity is hardly balanced. In this paper, connectivity and sparsity factors are considered while proposing the subspace clustering algorithm with post-process strategy. A non-dominated sorting algorithm is used for that selection of close neighbours that are defined as neighbours with high coefficient and common neighbours. Further, pruning of useless, incorrect or reserved connections based on the coefficients between the close and sample neighbours are performed. Lastly, inter and intra subspace connections are reserved by the post-process strategy. In the field of IoT and image recognition, the conventional techniques are compared with the proposed post-processing strategies to verify its effectiveness and universality. The clustering accuracy may be improved in the IoT environment while processing the noise data using the proposed strategy as observed in the experimental results.

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