基于ROI的海洋卫星遥感图像压缩算法

针对海洋监视卫星遥感图像的特点，提出了一种基于感兴趣区域(ROI: Regions Of Interest)的自适应海洋遥感图像近无损压缩算法。对图像进行了形态Harr小波最大提升后，在一个分辨率低的高频子带中利用阈值和八邻域连通分析方法检测出目标，使用外接矩形与环面的交集来描述ROI，其他分辨率级的高频子带的ROI通过Mosaic放大得到。高频子带中的ROI采用Rice无损熵编码方法，非ROI进行比特平面编码。低频子带采用DPCM和Rice结合的无损编码方法。实验结果表明，该算法能有效地划分ROI，在中低比特率情况下，获得了比JPEG2000更好的重构图像质量，且没有明显的ROI分割痕迹。本文算法的计算复杂度较低，易于硬件实现,而且还具有自适应性和数据包独立的优点，适合于海洋监视卫星遥感图像近无损压缩应用。

A compression algorithm of remote sensing image based on ROI for ocean surveillance satellite applications

According to the characteristics of the remote sensing images for ocean surveillance satellite,an adaptive near-lossless compression algorithm based on ROI(Regions of Interest) is proposed.After maximum lifting of morphological Harr wavelet,the objects are detected by threshold method and connectivity analysis of eight adjacent regions.The ROI of high-frequency sub-bands is depicted by the intersection of enclosing rectangle and an annulus in the lowest-resolution and gained by mosaic magnification in other resolution levels.In high-frequency sub-bands,Rice lossless entropy encoder is used for ROI,and bit plane encoder for background region,while DPCM and Rice for low-frequency sub-band.The experimental results show that the algorithm can segmentalize the ROI effectively without any visible segmentation trace.The PSNR(Peak Signal Noise Ratio) of proposed algorithm is 2～5 dB higher than that of JPEG2000 at low and middle bit rate,and both are higher than 45 dB at high bit rate.The algorithm has the advantages of low complexity,higher adaptive ability and independent datum packet and is easy to be implemented in hardware.Moreover,the algorithm can adapt to near-lossless compression for ocean surveillance satellite image.