Paper
30 April 2016 Sub-pixel mapping of hyperspectral imagery using super-resolution
Shreya Sharma, Shakti Sharma, Krishna Mohan Buddhiraju
Author Affiliations +
Abstract
With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth’s surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth’s surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shreya Sharma, Shakti Sharma, and Krishna Mohan Buddhiraju "Sub-pixel mapping of hyperspectral imagery using super-resolution", Proc. SPIE 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, 98801Y (30 April 2016); https://doi.org/10.1117/12.2223598
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KEYWORDS
Hyperspectral imaging

Spatial resolution

Super resolution

Image resolution

Zoom lenses

Associative arrays

Remote sensing

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