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Digital Image Processing of Multispectral Data

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Remote Sensing Geology
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Abstract

Digital image processing deals with the technique of implementing changes in remote sensing data pattern for specific purposes. It can be carried out for a number of purposes such as: radiometric image correction, geometric image correction, image registration, image enhancement, image filtering, image transformation, colour enhancement, image fusion, 2.5 Dimensional visualization, image segmentation and classification.

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References

  • Arora MK, Mathur S (2001) Multi-source image classification using neural network in a rugged terrain. Geocarto Int 16(3):37–44

    Article  Google Scholar 

  • Arora MK, Shukla A, Gupta RP (2011) Digital information extraction techniques for snow cover mapping from remote sensing data. In: Singh VP, Singh P, Haritashya UK (eds) Encyclopedia of snow, ice and glacier. Springer, Dordrecht, pp 213–232

    Google Scholar 

  • Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogram Remote Sens 58:239–258

    Article  Google Scholar 

  • Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comp Geosci 10:191–203

    Article  Google Scholar 

  • Blaschke T (2010) Object based image analysis for remote sensing, ISPRS J Photogram Remote Sens 65(1):2–16. ISSN 0924-2716. http://dx.doi.org/10.1016/j.isprsjprs.2009.06.004

  • Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data. In: Proceedings of Fifth JPL airborne earth science workshop, summaries, Pasadena, California, vol 1. JPL Publication 95–1, pp 23–26, 23–26 Jan 1995

    Google Scholar 

  • Buchanan MD (1979) Effective utilization of colour in multidimensional data presentation. Proc Soc Photo Opt Instrument Eng 199:9–19

    Google Scholar 

  • Buchanan MD, Pendgrass R (1980) Digital image processing: can intensity hue and saturation replace red, green and blue? Electro-Opt Syst Design 12(3):29–36

    Google Scholar 

  • Byrne GF, Crapper PF, Mayo KK (1980) Monitoring land-cover change by principal component analysis of multitemporal Landsat data. Remote Sens Environ 10:175–189

    Article  Google Scholar 

  • Campbell NA (1996) The decorrelation stretch transform. Int J Remote Sens 17:1939–1949

    Article  Google Scholar 

  • Carper WJ, LilIesand TM, Kiefer RW (1990) The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm Eng Remote Sens 56(4):459–467

    Google Scholar 

  • Chander G, Markham BL, Helder DL (2009) Summary of current radiometric coefficients for Landsat MSS, TM, ETM+, and EO-1 OLI sensors. Rem Sens Environ 113:893–903

    Article  Google Scholar 

  • Chikara RS (1984) Effect of mixed pixels on crop proportion estimation. Remote Sens Environ 14:207–218

    Article  Google Scholar 

  • Condit CD, Chavez PS (1979) Basic concepts of computerised digital image processing for geologists. US Geol Surv Bull No. 1462, US Govt Printing Office, Washington DC, 16p

    Google Scholar 

  • Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37:35–46

    Article  Google Scholar 

  • Congalton RG, Green K (1993) A practical look at the sources of confusion in error matrix generation. Photogram Eng Remote Sens 59:641–644

    Google Scholar 

  • Crane RB (1971) Preprocessing techniques to reduce atmospheric and sensor variability in multispectral scanner data. In: Proceedings of 7th international symposium on remote sensing of environment, vol II. Ann Arbor, MI, pp 1345–1355

    Google Scholar 

  • Crosta AP, Moore JM (1989) Enhancement of Landsat Themetic Mapper imagery for residual soil mapping in SW Minas Gerais State, Brazil: a prospecting case history in Greenstone Belt Terrain. In: Proceedings of 7th thematic conference on remote sensing for exploration geology, Calgary, pp 1173–1187, 2–6 October 1989

    Google Scholar 

  • Curran PJ (1985) Principles of remote sensing. Longman, London

    Google Scholar 

  • Daily MI (1983) Hue-saturation-intensity split-spectrum processing of Seasat radar imagery. Photogramm Eng Remote Sens 49:349–355

    Google Scholar 

  • Davis LS (1975) A survey of edge detection techniques. Computer Graphics Image Proc-essing 4:248–270

    Article  Google Scholar 

  • Davis JC (1986) Statistics and data analysis in geology, 3rd edn. Wiley, New York, p 646

    Google Scholar 

  • Drury SA (2004) Image interpretation in geology, 3rd edn. Blackwell Sciences, Malden, p 304

    Google Scholar 

  • Evans C, Jones R, Svalbe I, Berman M (2002) Segmenting multispectral Landsat TM images into field units. IEEE Trans Geosci Remote Sens 40(5):1054–1064

    Article  Google Scholar 

  • Farag AA (1992) Edge-based image segmentation. Remote Sens Rev 6:95–122

    Article  Google Scholar 

  • Fisher PF, Pathirana S (1990) The evaluation of fuzzy membership of land cover classes in the suburb an zone. Remote Sens Environ 34:121–132

    Article  Google Scholar 

  • Foody GM (1992) A fuzzy sets approach to representation ofvegetation continua from remotely sensed data: an example from Lowland heath. Photogram Eng Remote Sens 58:221–225

    Google Scholar 

  • Foody GM (1995) Land cover classification by an artificial neural network with ancillary information. Int J Geog Inform Sys 9:527–542

    Article  Google Scholar 

  • Foody GM (1998) Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution. Int J Remote Sens 19(13):2593–2599

    Article  Google Scholar 

  • Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recog Lett 17:1389–1398

    Article  Google Scholar 

  • Gillespie AR (1980) Digital techniques of image enhancement. In: Seigal BS, Gillespie AR (eds) Remote sensing in geology. Wiley, New York, pp 139–226

    Google Scholar 

  • Gillespie AR, Kahle AB, Walker RE (1986) Color enhancement of highly correlated images: I-decorrelation and HIS contrast stretches, Remote Sens Environ 20:209–235

    Google Scholar 

  • Gillespie AR, Kahle AB, Walker RE (1987) Colour enhancement of highly correlated images: II-channel ratio and chromaticity transformation techniques. Remote Sens Environ 22:343–365

    Article  Google Scholar 

  • Gonzales RC, Woods RE (2008) Digital image processing, 3rd edn. Addison Wesley, Reading

    Google Scholar 

  • Gupta RP (2003) Remote sensing geology, 2nd edn. Springer, Berlin, 655 p

    Google Scholar 

  • Gupta RP, Tiwari RK, Saini V, Srivastava N (2013) A simplified approach for interpreting principal component images. Adv Remote Sens 2:111–119

    Article  Google Scholar 

  • Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67:786–804

    Article  Google Scholar 

  • Haralick RM, Fu K (1983) Pattern recognition and classification. In: Colwell RN (ed) Manual of remote sensing. American Society of Photogrammetry and Remote Sensing, Falls Church, VA, pp 793–805

    Google Scholar 

  • Harris JR, Murray R, Hirose T (1990) IHS transform for the integration of radar imagery and other remotely sensed data. Photogramm Eng Remote Sens 56:1631–1641

    Google Scholar 

  • Harris JR, David WV, Andrew NR (1999) Integration and visualization of geoscience data. In: Remote sensing for the earth sciences, manual of remote sensing, 3rd edn, vol 3. Am Society for Photogrammetry and Remote Sensing, pp 307–354

    Google Scholar 

  • Haydn R (1985) A concept for the processing and display of Thematic Mapper data. In: Proceedings of symposium on Landsat-4 science characterization early results. NASA publ 2355 Greenbelt, MD, pp 217–237

    Google Scholar 

  • Haydn R, Dalke GW, Henkel J, Bare JE (1982) Application ofthe IHS colour transform to the processing of multi sensor data and image enhancement. Proc Int Symp Remote Sens of Arid and Semi-Arid Lands, Cairo, pp 599–616

    Google Scholar 

  • Hepner GF, Logan T, Ritter N, Bryant N (1990) Artificial neural network classification using a minimal training set: comparison to conventional supervised classification. Photogramm Eng Remote Sens 56:469–473

    Google Scholar 

  • Hill J (1991) A quantitative approach to remote sensing: sensor calibration and comparison. In: Belward AS, Valenzuela CR (eds), pp 97–110

    Google Scholar 

  • Hord RM (1982) Digital image processing of remotely sensed data. Academic Press, New York, p 256

    Google Scholar 

  • Hsu S (1978) Texture-tone analysis for automated landuse mapping. Photogramm Eng Remote Sens 44:1393–1404

    Google Scholar 

  • Hu X, Tao CV, Prenzel B (2005) Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity and colour features. Photogramm Eng Remote Sens 71(12):1399–1406

    Article  Google Scholar 

  • Jensen JR (2005) Introductory digital image processing, 3rd edn. Prentice Hall, Englewood Cliffs, 379 p

    Google Scholar 

  • Justice C, Wharton SW, Holben BN (1981) Application of digital terrain data to quantify and reduce the topographic effect on Landsat data. Int J Remote Sens 2:213–230

    Article  Google Scholar 

  • Kahle AB (1980) Surface thermal properties. In: Siegal BS, Gillespie AR (eds) Remote sensing in geology. Wiley, New York, pp 257–273

    Google Scholar 

  • Key JR, Maslanik JA, Barry RG (1989) Cloud classification from satellite data using a fuzzy set algorithm: a polar example. Int J Remote Sens 10:1823–1842

    Article  Google Scholar 

  • Kowalik WS, Lyon RJP, Switzer P (1983) The effects of additive radiance terms on ratios of Landsat data. Photogram Eng Remote Sens 49:659–669

    Google Scholar 

  • Lillesand TM, Kiefer RW (1987) Remote sensing and image interpretation, 2nd edn, Wiley, New York, 721 pp

    Google Scholar 

  • Loughlin WP (1991) Principal component analysis for alteration mapping. Photogram Eng Remote Sens 57(9):1163–1169

    Google Scholar 

  • Maselli F, Conese G, Petkov L, Resti R (1992) Inclusion of prior probabilities derived from a nonparametric proeess into the maximum likelihood classifier. Photogramm Eng Remote Sens 58:201–207

    Google Scholar 

  • Mather PM (2010) Computer proeessing of remotely sensed images, an introduction, 2nd edn. Wiley, Chicester

    Google Scholar 

  • Mertens KC, Verbeke LPC, Westra T, De Wulf RR (2004) Subpixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sens Environ 91:225–236

    Article  Google Scholar 

  • Moik JG (1980) Digital processing of remotely sensed images. NASA SP-431, US Govt Printing Office, Washington, DC

    Google Scholar 

  • Pal NR, Pal SK (1993) A review of image segmentation techniques. Pattern Recogn 26(9):1277–1294

    Article  Google Scholar 

  • Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21–36

    Article  Google Scholar 

  • Peli T, Malah D (1982) A study of edge detection algorithms. Comput Graph Image Process 20:1–21

    Article  Google Scholar 

  • Pichel W, Bristor CL, Brower R (1973) Artificial stereo: a technique for combining multichannel satellite image data. Bull Am Meteorol Soc 54:688–690

    Google Scholar 

  • Pohl C, van Genderen JL (1998) Multisensor image fusion in remote sensing: concepts, methods and application. Int J Remote Sens 19:823–854

    Article  Google Scholar 

  • Pour AB, Hashim M (2011) Spectral transformation of ASTER data and the discrimination of hydrothermal alteration minerals in a semi-arid region, SE Iran. Int J Phys Sci 6(8):2037–2059

    Google Scholar 

  • Pratt WK (2007) Digital image processing, 4th edn. Wiley, New York

    Book  Google Scholar 

  • Prost GL (2013) Remote sensing for geoscientists, 3rd edn. CRC Press, 702 p

    Google Scholar 

  • Radoux J, Defourny P (2007) A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery. Remote Sens Environ 110(4):468–475

    Article  Google Scholar 

  • Richards JA, Jia X (2006) Remote sensing digital image analysis, 4th edn, Springer, Heidelberg, 363 p

    Google Scholar 

  • Rosenfeld A, Kak AC (1982) Digital picture processing, 2nd edn. Academic Press, Orlando

    Google Scholar 

  • Ruiz-Armenta JR, Prol-Ledesma RM (1998) Techniques for enhancing the spectral reponse of hydrothermal alteration minerals in Thematic Mapper images of Central Mexico. Int J Remote Sens 19(10):1981–2000

    Article  Google Scholar 

  • Russ JC (2011) The image processing handbook, 6th edn. CRC Press, Boca Raton

    Google Scholar 

  • Sabins FF Jr (2007) Remote sensing: principles and interpretation, 4th edn. Waveland Press, Long Grove, p 512

    Google Scholar 

  • Sawchuka AA (1978) Artificial stereo. App Optics 17:3869–3873

    Article  Google Scholar 

  • Schalkoff RJ (1992) Pattern recognition: statistical. Wiley, New York

    Google Scholar 

  • Schowengerdt RA (2007) Remote sensing: models and methods for image processing, 3rd edn. Academic Press, San Diego

    Google Scholar 

  • Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions. Int J Remote Sens 14:1159–1177

    Article  Google Scholar 

  • Shaw GB (1979) Local and regional edge detectors: some comparisons. Comput Graph Image Process 9:135–149

    Article  Google Scholar 

  • Siegal BS, Abrams MJ (1976) Geologic mapping using Landsat data. Photogram Eng Remote Sens 42:325–337

    Google Scholar 

  • Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens Environ 10:135–163

    Article  Google Scholar 

  • Swain PH (1978) Fundmentals of pattern recognition in remote sensing. In: Swain PH, Davis SM (eds) Remote sensing: the quantitative approach. Mcgraw Hill, New York, pp 136–187

    Google Scholar 

  • Tangestani MH, Moore F (2002) Porphyry copper alteration mapping at the Meiduk Area. Iran, Int J Rem Sens 23(22):4815–4825

    Article  Google Scholar 

  • Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2002) Superresolution land cover pattern prediction using a Hopfield neural network. Remote Sens Environ 79:1–14

    Article  Google Scholar 

  • Tauch R, Kähler M (1988) Improving the quality of satellite images maps by various processing techniques. In: International Archives of Photogrammetry Remote Sensing. Proceedings of XVI ISPRS Congress, Tokyo, Japan, pp IV238–IV247

    Google Scholar 

  • Thomas IL, Howorth R, Eggers A, Fowler ADW (1981) Textural enhancement of a circular geological feature. Photogram Eng Remote Sens 47:89–91

    Google Scholar 

  • Thome KJ, Gellman DI, Parada RJ, Biggar SF, Slater PN, Moran MS (1993) Absolute radiometric calibration of Thematic Mapper. SPIE Proc 600:2–8

    Google Scholar 

  • Tom Ch, Miller LD (1984) An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms. Photogram Eng Remote Sens 50:193–207

    Google Scholar 

  • Verhoeye J, Wulf RD (2002) Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sens Environ 79:96–104

    Article  Google Scholar 

  • Vincent RK (1997) Fundamentals of geological and environmental remote sensing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  • Wahi M, Taj-Eddine K, Laftouhi N (2013) ASTER VNIR & SWIR band enhancement for lithological mapping—a case study of the Azegour area (Western High Atlas, Morocco). J Environ Earth Sci 3(12):33–44

    Google Scholar 

  • Wang F (1990) Fuzzy supervised classification of remote sensing images. IEEE Trans Geo-sci Remote Sens 28:194–201

    Article  Google Scholar 

  • Welch R, Ehlers M (1987) Merging multiresolution SPOT HRV and Landsat TM data. Photogram Eng Remote Sens 53(3):301–303

    Google Scholar 

  • Wyszecki G, Stiles WS (1967) Color science. Wiley, New York

    Google Scholar 

  • Zhang H, Yang Z, Zhang L, Shen H (2014) Super-resolution reconstruction for multi-angle remote sensing images considering resolution differences. Remote Sens 6:637–657. doi:10.3390/rs6010637

    Article  Google Scholar 

  • Zhou Z, Civco DL, Silander JA (1998) A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int J Remote Sens 19:743–757

    Article  Google Scholar 

  • Zobrist AL, Blackwell RJ, Stromberg WD (1979) Integration of Landsat, Seasat and other geo-data sources. In: Proceedings of 13th international symposium on remote sensing environment, Ann Arbor, MI, pp 271–279

    Google Scholar 

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Gupta, R.P. (2018). Digital Image Processing of Multispectral Data. In: Remote Sensing Geology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55876-8_13

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