This paper presents a Prior Important Band (PIB) algorithm for the compression of hyper-spectral images. The PIB method endows some of the bands with high priority so that the quality of these bands after compression is better than other bands. The rationale behind this approach is that, the bands of a data cube have different amount of information. Some bands contain much more information and features than other bands. In the PIB algorithm, all bands are classified into four categories according to their importance and easiness for compression. For the simplicity of the compression algorithm, we choose spectral correlation and information amount as the main index. Bands of low spectral correlation and high information are selected as Important Bands. The benefit of this algorithm lies in that it treats the important bands with higher quality quantization, and other bands with comparatively low quality quantization, so that the information can be better preserved after compression. Experimental results illustrate that PIB hyper-spectral image compression algorithm would be suitable for most applications.
In this paper, we propose a technique that combined template matching and support vector machine for road identification from high-resolution aerial image. It is a model-driven approach that combines both the local and global criteria about the radiometry and geometry of linear structures interested. In this approach, the road center point is extracted by utilizing the general road model. Then the road center point is used as initial point for the template matching through which the road segment is obtained. The road characteristic is learned through the support vector machine that is based on the statistical learning theory. The support vector machine is a powerful learning method thatit can get high classification accuracy without too much training sample. These properties can be applied for extracting the road characteristics from few road samples. The support vector machine is used to extract the true road segment and remove the false road segment. The proposed approach has been experimented on high-resolution aerial image and its performance is satisfied.
Generally, the echo of radar can be regarded as a combination of useful signal and noise. In this paper, an efficient wavelet-based non-linear signal compression scheme is presented, which takes full advantage of the two-dimensional correlation consisted in the radar signal. The whole process begins with a 4-scale QMF decomposition, then thresholding, quantization, zerotree scan and finished with arithmetic coding. Since radar signal usually is corrupted by a great amount of noise, a particular process of de-noising is added to the compression. For typical radar signal, testing results show that when compressed at a ratio of 200, the signal is still good fairly. Further more, a new criterion fit for quality evaluation of radar signal, i.e. Morphological Fidelity, is proposed.
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