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In this paper a lossless compression method for hyperspectral image is given. RKLT-based scheme was first presented by combining with 3D prediction, principal component selection, positive mapping followed by a range coder. The proposed method avoids the float number of coefficient which can make it much more easier to be processed on hardware. Numerical experiments show that the proposed method outperforms...
In absence of prior knowledge of the pure signatures (endmembers) existing in a remotely sensed image which is often the case, the mean spectra of the pixel vectors directly extracted from the image scene are usually used in unmixing problems. This approach ignores some important statistical properties of the extracted samples, thus, leads to suboptimal solutions. This paper proposes a novel method...
Achieving high target recognition accuracy is a pursuing and challenging issue for hyperspectral data analysis. The complicated imaging environment and noise interference lead to heterogeneous spectra within the homogeneous object, which makes the current spectral target recognition methods be lack of robustness. In this paper, a robust spectral target recognition method is proposed based on the combined...
This paper proposes a method of feature selection and classification based on ant colony algorithm for hyperspectral remote sensing image. After all features are randomly projected on a plane, each ant stochastically selects a feature on the plane firstly, and then decides which route to be selected in terms of the criterion function among features. Whereafter the feature combination is formed. At...
This paper proposes a method of dimensionality reduction and classification based on ant colony algorithm for hyperspectral remote sensing image. The high-dimensional hyperspectral data space is decomposed into several low-dimensional data subspace by ant colony algorithm (ACA) in terms of the correlation between bands. Then principal component analysis is used in subspace to extract features, whereafter...
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