Abstract
Geographic Information System (GIS) stores large volumes of spectral data (raw facts) acquired by sensors located at the satellite and convert them into features and information in order to provide answers to many questions and for easy retrieval and display based on the user needs. The conversion of data to information involves a lot of processing. The preprocessing, especially, is required for the following reasons:
-
To restore the satellite image quality in the presence of known or unknown degradations and noises.
-
To extract or highlight hidden details in the satellite image.
-
To extract regions or statistical and nonstatistical features of interest for analysis and classification purposes.
-
To geometrically correct the images for mapping and georeferencing.
This chapter describes various image enhancement methods, noise removal methods, image stitching and interpolation methods, segmentation, multivariate image processing techniques, and other image transformations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dey, N., Bhatt, C., & Ashour, A. S. (2018). Big data for remote sensing: Visualization, analysis and interpretation. Cham: Springer.
Gonzalez, R., & Woods, R. (2008). Digital image processing. Upper Saddle River: Pearson Education India.
Gonzalez, R., Woods, R., & Eddins, L. (2009). Digital image processing using MATLAB. New Delhi: TATA McGraw-Hill Education.
Al-Amri, S. S., Kalyankar, N. V., & Khamitkar, S. D. (2010). A comparative study of removal noise from remote sensing image. arXiv preprint arXiv:1002.1148.
Bhosale, N. P., & Manza, R. R. (2013). Analysis of effect of noise removal filters on noisy remote sensing images. International Journal of Scientific & Engineering Research (IJSER), 4(10), 1151.
Bhosale, N. P., & Manza, R. R. (2012). A review on noise removal techniques from remote sensing images. In National Conference, CMS (Vol. 274).
Thanki, R. M., & Kothari, A. M. (2018). Digital image processing using SCILAB. Germany: Springer.
Samanta, S., Mukherjee, A., Ashour, A. S., Dey, N., Tavares, J. M. R., Abdessalem Karâa, W. B., … Hassanien, A. E. (2018). Log transform based optimal image enhancement using firefly algorithm for autonomous mini unmanned aerial vehicle: An application of aerial photography. International Journal of Image and Graphics, 18(4), 1850019.
Rekik, A., Zribi, M., Hamida, A. B., & Benjelloun, M. (2007). Review of satellite image segmentation for an optimal fusion system based on the edge and region approaches. International Journal of Computer Science and Network Security, 7(10), 242–250.
Wang, C. (2009). Large-scale 3D environmental modelling and visualisation for flood hazard warning. Doctoral dissertation, University of Bradford.
Prewitt, J. M., & Mendelsohn, M. L. (1966). The analysis of cell images. Annals of the New York Academy of Sciences, 128(3), 1035–1053.
Tsai, D. M., & Wang, H. J. (1998). Segmenting focused objects in complex visual images. Pattern Recognition Letters, 19(10), 929–940.
Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29(3), 273–285.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.
Cumani, A. (1991). Edge detection in multispectral images. CVGIP: Graphical Model and Image Processing, 53(1), 40–51.
Davies, E. R. (2004). Machine vision: Theory, algorithms, practicalities. Amsterdam: Elsevier.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 679–698.
Wang, C., Su, W., Gu, H., & Shao, H. (2012, October). Edge detection of SAR images using incorporate shift-invariant DWT and binarization method. In 2012 IEEE 11th International Conference on Signal Processing (ICSP) (Vol. 1, pp. 745–748). IEEE.
Solomon, C., & Breckon, T. (2011). Fundamentals of digital image processing: A practical approach with examples in Matlab (pp. 267–269). USA: Wiley.
Capel, D. (2004). Image mosaicing. In Image mosaicing and super-resolution (pp. 47–79). London: Springer.
Ahmed, S. (2014). Image mosaicing. Retrieved October, 2018, from https://www.slideshare.net/saddam12345/image-mosaicing.
Shi, W., Tian, Y., & Liu, K. (2007). An integrated method for satellite image interpolation. International Journal of Remote Sensing, 28(6), 1355–1371.
Moser, G., De Giorgi, A., & Serpico, S. B. (2016). Multiresolution supervised classification of panchromatic and multispectral images by Markov random fields and graph cuts. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5054–5070.
Kumar, D. N. (2014). Remote sensing. Retrieved July, 2018, from https://nptel.ac.in/courses/105108077/.
Kauth, R. J., & Thomas, G. S. (1976). The tasseled cap—A graphic description of the spectral-temporal development of agricultural crops as seen by LANDSAT. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University of West Lafayette, Indiana (pp. 4B-41–4B-51).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Borra, S., Thanki, R., Dey, N. (2019). Satellite Image Enhancement and Analysis. In: Satellite Image Analysis: Clustering and Classification. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-6424-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-6424-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6423-5
Online ISBN: 978-981-13-6424-2
eBook Packages: EngineeringEngineering (R0)