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Subpixel Mapping Technique of HSI

  • Liguo WangEmail author
  • Chunhui Zhao
Chapter
  • 2.3k Downloads

Abstract

Spatial resolution means the minimum target that the sensor can distinguish, or the ground area expressed by a pixel point in the image. It is one of the important indexes of assessing sensor performance and remote-sensing information, and also the important basis of identifying the land object shape and size.

Keywords

Hyperspectral Image Neighborhood Pixel Markov Random Field Little Square Support Vector Machine Hopfield Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Atkinson PM (1997) Mapping subpixel boundaries from remotely sensed images. Innovations GIS 4:166–180Google Scholar
  2. Atkinson PM (2005) Subpixel target mapping from soft-classified, remotely sensed imagery. Photogram Eng Remote Sens 71(7):839–846CrossRefGoogle Scholar
  3. Ge Y, Li S, Lakhan VC (2009) Development and testing of a subpixel mapping algorithm. IEEE Trans Geosci Remote Sens 47(7):2155–2164CrossRefGoogle Scholar
  4. Ling F, Du Y, Xiao F, Xue H, Wu S (2010) Super-resolution land-cover mapping using multiple subpixel shifted remotely sensed images. Int J Remote Sens 31(19):5023–5040CrossRefGoogle Scholar
  5. Mertens KC, Verbeke LPC, De Wulf RR (2003a) Sub-pixel mapping with neural networks: Real-world spatial configurations learned from artificial shapes. In: Proceedings of 4th international symposium on remote sensing of Urban Areas, Regensburg, Germany pp 117–121Google Scholar
  6. Mertens KC, Verbeke LPC, Ducheyne EI, De Wulf RR (2003b) Using genetic algorithms in sub-pixel mapping. Int J Remote Sens 24(21):4241–4247Google Scholar
  7. Mertens KC, Basets BD, Verbeke LPC, De Wulf RR (2006) A subpixel mapping algorithm based on subpixel/pixel spatial attraction models. Int J Remote Sens 27(15):3293–3310CrossRefGoogle Scholar
  8. Nguyen MQ, Atkinson PM, Lewis HG (2005) Superresolution mapping using a Hopfield neural network with LIDAR data. IEEE Geosci Remote Sens Lett 2(3):366–370CrossRefGoogle Scholar
  9. Shen Z, Qi J, Wang K (2009) Modification of pixel-swapping algorithm with initialization from a subpixel/pixel spatial attraction model. Photogram Eng Remote Sens 75:557–567CrossRefGoogle Scholar
  10. Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2001) Super-resolution target identification from remotely sensed images using a hopfield neural network. IEEE Trans Geosci Remote Sens 39(4):781–796CrossRefGoogle Scholar
  11. Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2002) Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sens Environ 79(1):1–14CrossRefGoogle Scholar
  12. Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2003) Increasing the spatial resolution of agricultural land cover maps using a hopfield neural network. Int J Geogr Inf Sci 17(7):647–672CrossRefGoogle Scholar
  13. Teerasit K, Arora MK, Varshney PK (2005) Super-resolution land-cover mapping using a Markov random field-based approach. Remote Sens Environ 96(3–4):302–314Google Scholar
  14. Tolpekin VA, Hamm NAS (2008) Fuzzy super resolution mapping based on Markov random fields. In: Proceedings of international geoscience and remote sensing symposium, pp 875–878Google Scholar
  15. Tolpekin VA, Stein A (2009) Quantification of the effects of land-cover-class spectral separability on the accuracy of markov-random-field-based superresolution mapping. IEEE Trans Geosci Remote Sens 47(9):3283–3297CrossRefGoogle Scholar
  16. Verhoeye J, De Wulf RR (2002) Land-cover mapping at subpixel scales using linear optimization techniques. Remote Sens Environ 79(1):96–104CrossRefGoogle Scholar
  17. Wang L, Zhang Y, Li J (2006) BP neural network-based sub-pixel mapping method. In: International conference on intelligent computing, pp 755–760Google Scholar
  18. Wang L, Jia X, Zhang Y (2007) A novel geometry-based feature-selection technique for hyperspectral imagery. IEEE Geosci Remote Sens Lett 4(4):171–175CrossRefGoogle Scholar
  19. Zhang L, Wu K, Zhong Y, Li P (2008) A new subpixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing 71(10–12):2046–2054CrossRefGoogle Scholar

Copyright information

© National Defense Industry Press, Beijing and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Harbin Engineering UniversityHarbinChina

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