Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information
Abstract— Hybrid Compression of Hyperspectral Images Based on PCA With Pre-Encoding Discriminant Information. It has been shown that image compression based on principal component analysis < Final Year Projects 2016 > PCA provides good compression efﬁciency for hyper spectral images. However, PCA might fail to capture all the discriminant information of hyper spectral images, since features that are important for classiﬁcation tasks may not be high in signal energy. To deal with this problem, we propose a hybrid compression method for hyper spectral images with pre-encoding discriminant information. A feature extraction method is ﬁrst applied to the original images, producing a set of feature vectors that are used to generate feature images and then residual images by subtracting the feature-reconstructed images from the original ones.
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