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A new sub-pixel mapping algorithm based on a BP neural network with an observation model
- Source :
-
Neurocomputing . Jun2008, Vol. 71 Issue 10-12, p2046-2054. 9p. - Publication Year :
- 2008
-
Abstract
- Abstract: The mixed pixel is a common problem in remote sensing classification. Even though the composition of these pixels for different classes can be estimated with a pixel un-mixing model, the output provides no indication of how such classes are distributed spatially within these pixels. Sub-pixel mapping is a technique designed to use the output information with the assumption of spatial dependence to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. This paper proposes a new algorithm based on a back-propagation (BP) network combined with an observation model. This method provides an effective method of obtaining the sub-pixel mapping result and can provide an approximation of the reference classification image. With the upscale factor, the model was tested on both a simple artificial image and a remote sensing image, and the results confirm that the proposed mapping algorithm has better performance than the original BPNN model. [Copyright &y& Elsevier]
- Subjects :
- *ALGORITHMS
*ALGEBRA
*FOUNDATIONS of arithmetic
*PIXELS
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 71
- Issue :
- 10-12
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 32555585
- Full Text :
- https://doi.org/10.1016/j.neucom.2007.08.033