Pixel-level image fusion techniques in remote sensing: a review

Solanky, Vijay; Katiyar, Sunil Kumar

doi:10.1007/s41324-016-0046-6

Cited by 22 publications

(2 citation statements)

References 11 publications

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“…where m 1 ({A})(x) represents mass value for a pixel x belonging to A (A ∈ 2 Ω ) according to the spectral model and m 2 ({A})(x) is the counterpart from the supervised model. In order to make decision for both singletons and ignorance, the Appriou's rule is applied in our approach, as illustrated previously in equation (7). For the coefficients in equation ( 8), K d and λ x are equal to 1 and r was chosen as 0.1. results.…”

Section: Modelmentioning

confidence: 99%

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An automatic water detection approach based on Dempster-Shafer theory for multi-spectral images

Martin

Estival

2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to find appropriate thresholds manually. As for traditional machine learning methods, they identify water merely via differences of spectra of various land covers, without taking specific properties of spectral reflection into account. In this paper, we propose an automatic approach to detect water bodies based on Dempster-Shafer theory, combining supervised learning with specific property of water in spectral band in a fully unsupervised context. The benefits of our approach are twofold. On the one hand, it performs well in mapping principle water bodies, including little streams and branches. On the other hand, it labels all objects usually confused with water as 'ignorance', including half-dry watery areas, built-up areas and semi-transparent clouds and shadows. 'Ignorance' indicates not only limitations of the spectral properties of water and supervised learning itself but insufficiency of information from multi-spectral bands as well, providing valuable information for further land cover classification.

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Section: Modelmentioning

confidence: 99%

“…Fusion in the pixel level consists in considering different original data from multiple sensors as the data from one signal source with single resolution, making data more informative than an individual source [7]. In the feature level, several features (e.g.…”

Section: Introductionmentioning

confidence: 99%