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nIn this work, we develop a new modelling process to optimize the PV installation layout on industrial building rooftops; the model searches for maximizing the number of installed PV panels in a defined spot area, characterized by its shape, size, tilt and orientation. The study focuses on areas of latitude between tropics, but is useful for other latitudes. The method is applied to an industrial conglomerate of reference, made up of 34 industries, whose geographical characteristics have been provided by the GIS technique. The proposed model reduces the area of the spot to a very small size (5m x 5m), thanks to the use of the GIS tools, thus increasing the precision of results and making the solution more reliable. The accuracy of the method is over 98.5%. The model takes into account not only the shape and size of the PV panel but also the tilt angle. The model predicts the number of panels as a function of the tilt angle of the panel and the azimuth of the rooftop. The modelling process has allowed obtaining the surface coverage factor of the PV layout for panels facing to the Equator in rooftops with a specific azimuth. The results of the modelling shows that coverage factor increases with the rooftop’s azimuth, having a minimum value of 75% for a rooftop azimuth of 45° and a maximum of 92% for angles of 5° and 85°.n

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nDolomite CaMg (Co3)2, is the common rock forming mineral and composed of the carbonates of calcium and magnesium with an equivalent proportion. Dolomite is the primary integral of the sedimentary rock called dolostone and the metamorphic rock called as dolomitic marble. Our method is allowing to confidently determine the location and their boundaries of local geological structures that are likely to contain dolomite resources in the study area of Anantapur, AP, India. Geospatial techniques like Remote Sensing and Geographical Information System are very useful for the estimation of natural resources using the Band rationing techniques.n

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1. Abdullah A, Nassr S, Ghaleeb A. Landsat ETM-7 for lineament mapping using automatic extraction technique in the SW part of Taiz Area, Yemen. Global J Human Social Sci. 2013; 13(3): 35–37p.
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nThe applications of hyperspectral images (HSI) are many, which include agriculture, food quality, remote sensing, medical diagnostics and safety assessment. Hyperspectral image analysis has been used for detecting contaminants and identifying defects in food. It also utilizes advanced software and hardware tools hence allowing users to diagnose and detect pathologies. In this paper an avant-garde investigation about Hyperspectral image compression and classification techniques which can be used in various applications like broadcasting of television, remote sensing via satellite, storage and classification of medical images, pictures and documents has been made. Significant increase in multimedia products has created a need to enhance, extract, store and interpret the information received in the most effective manner. The size of a Hyperspectral image comprises approximately 138.81 megabytes and hence requires large space for storage. Hence, Hyperspectral image compression is of great importance as it reduces the data redundancy and the hardware space required for storage. Hyperspectral image classification has gained great research attention due to the increasing demand of feature information extraction. This survey focuses on describing the recent advances in spectral spatial classification of hyperspectral images and various recent advancements in compression techniques for input HSIs.n

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1. Gogineni R, Chaturvedi A. Hyperspectral Image Classification. Processing and Analysis of Hyperspectral Data. Book Chapter. Edited. Volume. 2020. doi: 10.5772/intechopen.88925.
2. Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A. Modern trends in hyperspectral image analysis: a review. IEEE Access. 2018;6:14118–29. doi: 10.1109/ACCESS.2018.2812999.
3. Sucharitha B, Anithasheela K. Hybrid compression method for hyper spectral images using singular value decomposition and discrete wavelet transform. IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT). Nov 13–14 2021; Visakhapatnam, India, US: IEEE Press. Vol. 2022; 2021. doi: 10.1109/ICISSGT52025.2021.00032.
4. Fauvel M, Tarabalka Y, Benediktsson JA, Chanussot J, Tilton JC. Advances in spectral-spatial classification of hyperspectral images. Proc IEEE. Proceedings of the IEEE. 2013, 101 (3):652–75. doi: 10.1109/JPROC.2012.2197589.
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6. Ugur Toreyin YB, Yılmaz O, Mert YM, Turk F. Lossless hyperspectral image compression using wavelet transform based spectral decorrelation 7th International Conference on Recent Advances in Space Technologies (RAST). Jun 16–19 2015; Istanbul, Turkey. Vol. 2015. IEEE Publications; 2015. p. 251–4.
7. Xue Jize, Zhao Y, Liao W, Chan JC-W. Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. Remote Sens. 2019;11(2):193. doi: 10.3390/rs11020193.
8. Guerra R, Mar ́, Íaz ́D, Barrios Y, Ópez SL, Sarmiento R. A Hardware-Friendly Algorithm for the on-board Compression of hyperspectral Images 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Sep 23–26 2018; Amsterdam, Netherlands, US. IEEE Publications; 2018. p. 2019.
9. Karami A, Yazdi M, Mercier G. Compression of Hyperspectral Images Using Discrete Wavelet Transform and Tucker Decomposition. IEEE J Sel Top Appl Earth Observations Remote Sensing. April 2012;5(2):444–50. doi: 10.1109/JSTARS.2012.2189200.
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18. Cao Xiangyong, Zhou F, Xu L, Meng D, Xu Z, Paisley J. Hyperspectral image classification with Markov random fields and a convolutional neural network. IEEE Trans Image Process. May 2018;27(5):2354–67. doi: 10.1109/TIP.2018.2799324, PMID 29470171.
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