According to the characteristics of the remote sensing images from ocean surveillance satellite

an adaptive near-lossless compression algorithm based on ROI (Regions Of Interest) was proposed. After maximum lifting of morphological Harr wavelet

the objects were detected by threshold and connectivity analysis of eight adjacent regions in the lowest-resolution high-frequency sub-bands. ROI was depicted by intersection of enclosing rectangle and an annulus. In other resolution levels

the ROI of high-frequency sub-bands can be gained by mosaic magnification. In high-frequency sub-bands

Rice lossless entropy encoder was used for ROI

and bit plane encoder for background region

while DPCM and Rice for low-frequency sub-band. Experiments show that the algorithm could segmentalize the ROI effectively

gain better quality of reconstructed image compared with JPEG2000 and there is no visible segmentation trace. The algorithm possesses low complexity and is easy to be implemented in hardware. Moreover

it has the merits of adaptability

and independent data packet. So the algorithm can adapt to ocean surveillance satellite’s near-lossless compression applications.