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A LITERATURE REVIEW ON HYPERSPECTRAL IMAGE DENOISING
<span title="2020-11-01">2020</span>
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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/namhphsg6rdvtofp27o3scimoy" style="color: black;">International Journal of Engineering Applied Sciences and Technology</a>
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Hyperspectral images are threedimensional images. These consist of two types of domains. They were spatial domain and spectral domain. Hyperspectral image contains very much contamination while capturing from a spectral camera. These images can be captured at a particular wavelength using the electromagnetic spectrum. To eliminate this noise various techniques are there. A few of them are 1. Global low-rank representation 2. Local low-rank representation, 3. Sparse representation etc. while
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... inating this noise using various techniques, we can apply for many problems like target detection, material identification on the earth's surface, and agriculture field. Here we have discussed and analyzed with opposing approaches and existing strategies to solve the problems. Denoising approaches have thus been the key step toward developing object identification and classification in remote sensing imaging applications and military applications.
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