遥感图像准无损压缩技术的研究

提出了一种基于图像恢复技术的遥感图像压缩技术，在提高遥感图像压缩比的同时，使压缩后的重建图像质量达到准无损的技术要求。对SRTF、TM图像的试验结果表明，该技术是可行的。

The Technique of Guasi-lossless Compression of Remote Sensing Image

With the rapid development of aerial and space remote sensing technique, the digital camera has been explored, and mapping experiments with this kind of digital images are processing as the regular mede of aerophotogrammetry. On the other hand, the technique of integration of 3S (GPS,RS and GIS) and digital photOgrammetric system have been put in actual use as a mature technique. Meanwhile, the information superhighway will be the new key technique in the coming 21 century. All these developments mean that for the spatial geographical data require muti-level remte sensing image data. In RS, GIS and DPS(digital photoprammetric system), one of key techniques is how to deal with the real time transmitting of huge remote sensing data and how to build image data base. So the investigation technique of compression of remote sensing data is avery urgent task. Normal data endriing technique comes to loss-less comression, while the compression ratio only ranger about 2 and can't be satisfied with actual repuirement. The new developing compression techniques, such as wavelet compression and fractal compression will reach a high compression ratio, but they belong to degraded compression and need much moe CPU time.So for the time being, these metheds have not much actual use in the field of the remote sensing.In this paPer, the technique of guasi-lossless compression based on the itnage restoration is presented. The technique of compression described here will include three stePs, namely, bit compression, removeing correlation and image restoration based on the thcory of medulation transfer function(MTF). The guasi-lossless compression comes to high speed and the quality of reconstructed image under restoration reached the quasi-lereless level with higher compression ratio.The test of TM and SPOT remote sensing itnages show that the average comression ratio is abeut 4~5, the fidelity reaches 0. 99 and Peak value signal-noise ratio(PSNR) is over 42. All of the results confirm that the technique is reasonable and applicable.