subpixel mapping algorithm combining pixel-level and subpixel-level spatial dependences with binary integer programming

A new subpixel mapping (SPM) algorithm combining pixel-level and subpixel-level spatial dependences is proposed in this letter. The pixel-level dependence is measured by the spatial attraction model (SAM) with either surrounding or quadrant neighbourhood, while the subpixel-level dependence is chara...

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Veröffentlicht in:Remote sensing letters Jg. 5; H. 10; S. 902 - 911
Hauptverfasser: Chen, Yuehong, Ge, Yong, Wang, Qunming, Jiang, Yu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 03.10.2014
Taylor & Francis Ltd
Schlagworte:
Algorithms
Applied geophysics
Dependence
Earth sciences
Earth, ocean, space
Exact sciences and technology
Heuristic
Integer programming
Internal geophysics
land cover
Mapping
Mathematical models
Objective function
Optimization
Remote sensing
Spatial distribution
system optimization
algorithms
models
optimization
Space remote sensing
accuracy
remote sensing
spatial distribution
methodology
land cover
Pixel
ISSN:2150-7058, 2150-704X, 2150-7058
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Abstract A new subpixel mapping (SPM) algorithm combining pixel-level and subpixel-level spatial dependences is proposed in this letter. The pixel-level dependence is measured by the spatial attraction model (SAM) with either surrounding or quadrant neighbourhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence in the constructed objective function. The branch-and-bound algorithm is employed to solve the optimization problem, and thus, obtain the optimal spatial distribution of subpixel classes. An artificial image and a set of real remote sensing images were tested for validation of the proposed method. The results demonstrated that the proposed method can achieve results with greater accuracy than two traditional SPM methods and the mixed SAM method. Meanwhile, the proposed method needs less computation time than the mixed SAM, and hence it provides a new solution to subpixel land cover mapping.
AbstractList A new subpixel mapping (SPM) algorithm combining pixel-level and subpixel-level spatial dependences is proposed in this letter. The pixel-level dependence is measured by the spatial attraction model (SAM) with either surrounding or quadrant neighbourhood, while the subpixel-level dependence is characterized by either the mean filter or the exponential weighting function. Both pixel-level and subpixel-level dependences are then fused as the weighted dependence in the constructed objective function. The branch-and-bound algorithm is employed to solve the optimization problem, and thus, obtain the optimal spatial distribution of subpixel classes. An artificial image and a set of real remote sensing images were tested for validation of the proposed method. The results demonstrated that the proposed method can achieve results with greater accuracy than two traditional SPM methods and the mixed SAM method. Meanwhile, the proposed method needs less computation time than the mixed SAM, and hence it provides a new solution to subpixel land cover mapping.
Author Wang, Qunming
Ge, Yong
Chen, Yuehong
Jiang, Yu
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Keywords algorithms
models
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Space remote sensing
accuracy
remote sensing
spatial distribution
methodology
land cover
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Snippet A new subpixel mapping (SPM) algorithm combining pixel-level and subpixel-level spatial dependences is proposed in this letter. The pixel-level dependence is...
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SubjectTerms Algorithms
Applied geophysics
Dependence
Earth sciences
Earth, ocean, space
Exact sciences and technology
Heuristic
Integer programming
Internal geophysics
land cover
Mapping
Mathematical models
Objective function
Optimization
Remote sensing
Spatial distribution
system optimization
Title subpixel mapping algorithm combining pixel-level and subpixel-level spatial dependences with binary integer programming
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