Abstract
Image classification is a key task in many remote sensing applications. As discussed in Sect. 2.6 of Chap. 2, the objective of classification is to allocate each pixel of a remote sensing image into only one class (i.e. hard or per-pixel classification) or to associate the pixel with many classes (i.e. soft, sub-pixel or fuzzy classification). A number of hard classifiers are in vogue based on approaches such as statistical (Mather 1999), neural networks (Foody 2000a) and decision tree (Hansen et al. 2001).
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References
Aplin P, Atkinson PM (2001) Sub-pixel land cover mapping for per-field classification. International Journal of Remote Sensing 22 (14): 2853–2858
Atkinson PM (1997) Mapping sub-pixel boundaries from remotely sensed images. Innovation in GIS 4: 166–180
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences 10: 191–203
Binaghi E, Brivio PA, Ghezzi P, Rampini A (1999) A fuzzy set-based accuracy assessment of soft classification. Pattern Recognition Letters 20: 935–948
Bremaud P (1999) Markov chains Gibbs field, Monte Carlo simulation and queues. Springer Verlag, New York
Brown M, Lewis HG, Gunn SR (2000) Linear spectral mixture models and support vector machines remote sensing. IEEE Transactions on Geosciences and Remote Sensing 38: 2346–2360
Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. International Journal of Remote Sensing 17: 1317–1340
Foody GM (1998) Sharpening fuzzy classification output to refine the representation of subpixel land cover distribution. International Journal of Remote Sensing 19 (13): 2593–2599
Foody GM (2000a) Mapping land cover form remotely sensed data with a softened feedforward neural network. Journal of Intelligent and Robotic System 29: 433–449
Foody GM (2000b) Estimation of sub-pixel land cover composition in the presence of untrained classes. Computers and Geosciences 26: 469–478
Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recognition Letters 17 (13): 1389–1398
Foody GM, Cox DP (1994) Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing 15: 619–631
Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing 17: 1075–1081
Ibrahim MA, Arora MK, Ghosh SK (2003) A comparison of FCM and PCM for sub-pixel classification. Proceedings of 1St Indian International Conference on Artificial Intelligence, Hyderabad, India. CD
Mather PM (1999) Computer processing of remotely-sensed images: an introduction. Wiley, Chichester
Schneider W (1993) Land use mapping with subpixel accuracy from Landsat TM image data. Proceedings of 25th International Symposium on Remote Sensing and Global Environmental Change, Ann Arbor, MI, pp 155–161.
Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing 14: 1159–1177
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2001) Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geosciences and Remote Sensing 39 (4): 781–796
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2002) Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment 79: 1–14
Van Trees HL (1968) Detection, estimation and modulation theory. Wiley, New York Varshney PK ( 1997 ) Distributed detection and data fusion. Springer Verlag, New York
Verhoeye J, Wulf RD (2002) Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment 79: 96–104
Winkler G (1995) Image analysis random fields and dynamic Monte Carlo methods. Springer Verlag, New York
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Kasetkasem, T., Arora, M.K., Varshney, P.K. (2004). An MRF Model Based Approach for Sub-pixel Mapping from Hyperspectral Data. In: Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05605-9_12
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DOI: https://doi.org/10.1007/978-3-662-05605-9_12
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