Research On The Method Of Hyperspectral Image Compression

Hyperspectral remote sensing is gainning more attention in the fields of military surveillance and national economy. However, the applications of hyperspectral image data are still in their infancy as handling the significant size of the data presents a challenge for the user community. The request for efficient compression methods becomes pressing, since technological progresses make higher and higher spatial and spectral resolutions available. In this dissertation, algorithms of hyperspectral remote sensing image compression and performance evaluation of lossy compression methods are proposed.The lossless compression mthods using unsupervised classification as pretreatment step are studied at first. Considering the detailed texture of hyperspectral image, a predicting scheme using adjacent pixels in the same cluster is presented based on clustering and bands reordering. According to the spectral and spatial correlation characteristic, another predicting scheme using single adjacent pixel and self location pixel in multi-bands as predicting data is proposed. A multi-predictor frame with one from four strategy is presented consequently. The experimental results show the validity of the proposed algorithms.In some cases, especially in satellite data link, limited by the available bandwidth and the onboard storage capacity, lossy compression techniques have to be used. The evaluation of the quality of reconstructed data becomes a new issure. In chapter 3 of this dissertation, the existent evaluation methods are classified and summarized. Combined with the spectra classifying application, an evaluation scheme called optimal performance is developed. The relationship between distortion criteria and the preciseness of applications is discussed. And a robust criteria extraction method is developed based on imaging data of various scenes. Three lossy compression approaches using vector quantization are used to show the evaluation procedure. Both of the evaluation schemes have opening framework. Combined with engineering practice, a testing system for high speed data compression equipments is presented. The system is designed flexibly, and by simple changing the hardware and software, the system can be used to test the speed and error control performance of hyperspectral data compression equipments.Bands grouping idea is introduced to the WKV (two dimensional Wavelet transform and Kronecker gain shape Vector quantization) lossy compression method in chapter 4. An equal length grouping and a locally optimized grouping are used. And a new method called GWKV (Grouping WKV) is developed. Compared with the original scheme, the new method has merits of lower complexity, lower storage space and good parallelizability. The experimental results show that the algorithm is efficient.In military surveillance, the application focuses on the detection of small and anomalous targets. In some cases, the image reconstruction is not necessary. Compression methods based on independent component analysis (ICA) are discussed in chapter 5. Two geometric endmember extraction methods: unsupervised orthogonal subspace projection (UOSP) and maximum distance extractor, and RX anomality detector,are introduced to the mixing matrix initialization of FastICA. Combined with endmember extraction, a "conservative" revising algorithm of the estimation of virtual dimensionality (VD) is proposed to satisfy the need of small targets detection. The compression method called RVEIS-STD is developed. In the experiments, the validity of the modified CEM (constrained energy minimum) operator when it is used on independent components is studied. We construct two small targets in the AVIRIS data of an airfield, and detect the small targets successfully. The results approve that the RVEIS-STD compression method is an efficient compression approach in small targets and anomalies detecting applications. Simultineously, the results also validate the key effect of the revising algorithm in small characteristics protection.In the end, the direction of further study is pointed out, though, in personal opinion.