True Sensor Captured Radiance and Surface Reflectance from Landsat-8 Multispectral Image Sets
C. Rajabhushanam1, Allin Geo A.V2, Pothumani S3
1C.Rajabhushanam, Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Allin Geo A.V, Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3Pothumani S, Assistant Professor, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 13 September 2019 | Revised Manuscript received on 22 September 2019 | Manuscript Published on 10 October 2019 | PP: 166-170 | Volume-8 Issue-6S2 August 2019 | Retrieval Number: F10430886S219/19©BEIESP | DOI: 10.35940/ijeat.F1043.0886S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Proper validation of multispectral image sets that are produced by Satellite Sensors, in this era of big-data computing, is an important aspect in signal processing of natural semantic scenes. In this research article we explore fundamental issues that are cropped up in radiance correction and surface reflectance measurements. We calibrate the raw Landsat-8 imagery digital numbers by using standard functional oriented modular programs in R software, for true sensor radiance and reflectance values. This important preprocessing step is essential for activities that use multispectral images for natural resources monitoring and change-detection studies. It is noted that the procedures for preprocessing can be applied in machine learning algorithms for scientific studies.
Keywords: Satellite Sensors, Algorithms, Semantic Scenes.
Scope of the Article: Image Security