Enhancing spatial resolution for exploitation in hyperspectral imagery

Rhody, Harvey

doi:10.1109/aipr.2002.1182250

Cited by 3 publications

(2 citation statements)

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“…This specific aspect of data fusion is often referred to as data merge or band-sharpening [4][5][6]. More specifically, sharpened multi-spectral data is a fusion product in which the multi-spectral bands are sharpened via the higher-resolution panchromatic image.…”

Section: Introductionmentioning

confidence: 99%

Fusion of multi-spectral and panchromatic images using fuzzy rule

Yang

Jing

Liu

et al. 2007

Communications in Nonlinear Science and Numerical Simulation

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Section: Introductionmentioning

confidence: 99%

Fusion of multi-spectral and panchromatic images using fuzzy rule

Yang

Jing

Liu

et al. 2007

Communications in Nonlinear Science and Numerical Simulation

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“…Some related works have been carried out to exploit higher spatial resolution in remotely sensed multispectral or hyperspectral images [4][5][6][7][8][9][10][11]. The proposed techniques are based on approaches such as fusion of hyperspectral images with high spatial resolution of panchromatic images [6] or evaluating the statistical significance of the pixels [11].…”

Section: Introductionmentioning

confidence: 99%

A classification enhancement in hyperspectral imagery using superresolution technique

Mianji

Zhang

Hosseinipanah

2008

2008 9th International Conference on Signal Processing

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In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low spatial resolution frames of different wavelengths of the hyperspectral image are fed to a quadratic programming based classification algorithm to enhance the spatial resolution of the classification process. The results show a better classification and an edge improvement. Target recognition is the main field which can benefit from this technique.

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