Containing a Subpixel Target
Abstract—Compression of Hyperspectral Images Containing a Subpixel Target. Hyperspectral (HS) image sensors measure the reflectance of each pixel at a large number of narrow spectral bands, creating a three-dimensional representation of the captured scene. The HS image (HSI) consumes a great amount of storage space and transmission time. Hence, it would be desirable to reduce the image representation to the extent possible using a compression method appropriate to the usage and processing of the image. Many compression methods have been proposed aiming at different applications and fields. This research focuses on the lossy compression of images that contain subpixel targets. This target type requires minimum compression loss over the spatial dimension in order to preserve the target, and the maximum possible spectral compression that would still enable target detection. For this target type, we propose the PCA-DCT (principle component analysis followed by the discrete cosine transform) compression method. It combines the PCA’s ability to extract the background from a small number of components, < Final Year Projects > with the individual spectral compression of each pixel of the residual image, obtained by excluding the background from the HSI, using quantized DCT coefficients. The compression method is kept simple for fast processing and implementation, and considers lossy compression only on the spectral axis.
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