Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Remote Sensing by using Unsupervised Algorithm

Version 1 : Received: 9 July 2021 / Approved: 12 July 2021 / Online: 12 July 2021 (12:14:58 CEST)

How to cite: Saleem, A. Remote Sensing by using Unsupervised Algorithm. Preprints 2021, 2021070257. Saleem, A. Remote Sensing by using Unsupervised Algorithm. Preprints 2021, 2021070257.


Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.


Hyperspectral images, unsupervised Algorithm, clustering,K-means algorithm, spectral signature.


Environmental and Earth Sciences, Remote Sensing

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