Abstract
The book contributes to the subject area of remote sensing and Geographic Information System. It is focused on the study and analysis of automated cloud detection and removal of satellite imagery using the selection of thresholds value for various spectral tests in the perspective of RSGIS (Ramya, KarthiPrem, Nithyasri in IJIACS 3(2), [1], Rafael, Richard in Digital image processing. Prentice Hall, [2]). A significant obstacle of extracting information using satellite imagery is the presence of clouds. Removing these portions of image and then filling in the missing data is an important image-editing task. Traditionally, the objective is to cut the cloudy portions out from the frame and fill in the gaps with clear patches from similar images taken at different time. Remote sensing is providing opportunities in various branches of environmental research. The fields of application for multi-spectral remote sensing instruments in earth observation are monitoring the forests, oceans or urban areas over agricultural applications to the extent of natural resources. A significant prerequisite for analysis of earth observation data is the information that is free from external influences and disturbances. One possible cause of data loss is cloud cover of satellite imagery. Cloud cover is recognized as a significant loss of data and information quality by many scientific studies. The existence of cloud cover is the loss of meaningful data and information because they are a considerable source of uncertainty with regard to the application of any algorithm aiming for the retrieval of land surface (Zakaria, Ibrahim, Suandi in A review: image compensation techniques. pp. 404–408, [3], Sengee, Sengee, Choi in IEEE Trans Consum Electron 56(4):2727–2734, [4], Hardin, Jensen, Long, Remund in Testing two cloud removal algorithms for SSM/I [5]).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ramya P, KarthiPrem S, Nithyasri A (2014) Cloud removal in high resolution satellite images using discrete wavelet transform. IJIACS 3(2). ISSN 2347–8616
Rafael CG, Richard EW (2002) Digital image processing, 2nd edn. Prentice Hall (2002)
Zakaria MF, Ibrahim H, Suandi SA (2010) A review: image compensation techniques. In: 2nd international conference on computer engineering and technology, vol 7, pp 404–408
Sengee N, Sengee A, Choi H-K (2010) Image contrast enhancement using bi-histogram equalization with neighborhood metrics. IEEE Trans Consum Electron 56(4):2727–2734 (2010)
Hardin PJ, Jensen RR, Long DG, Remund QP (1999) Testing two cloud removal algorithms for SSM/I
Teillet PM, Fedosejevs G (1995) On the dark target approach to atmospheric correction of remotely sensed data. Can J Remote Sens 21(4):374–387
Biday S, Bhosle U (2009) Relative radiometric correction of cloudy multitemporal satellite imagery. Int J Electr Comput Energ Electron Commun Eng 3(3):472–746
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, S., Das, P., Roy, B.R. (2020). Cloud Detection and Cloud Removal of Satellite Image—A Case Study. In: Sarma, H., Bhuyan, B., Borah, S., Dutta, N. (eds) Trends in Communication, Cloud, and Big Data. Lecture Notes in Networks and Systems, vol 99. Springer, Singapore. https://doi.org/10.1007/978-981-15-1624-5_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-1624-5_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1623-8
Online ISBN: 978-981-15-1624-5
eBook Packages: EngineeringEngineering (R0)