Research On Image Compression For Reginon Of Interest Of Remote Sensing Image Based On CCSDS

With the development of space remote sensing technology, the resolution ofoptical remote sensing image is more and more high, and the data obtained in a shortperiod is increasing. However, the backward technology of transmission and storagefor remote sensing can’t meet the increasing image data. So, the images should becompressed before being transmitted. Usually, people only focus on a small part ofan image, which is called region of interest, while the other parts known asbackground region. When compressing an image, lossless compression or losscompression with a low ratio for the region of interest can be adopted, and thebackground region can be compressed using loss compression with a high ratio. As aresult, it not only reduces the requirements of the image transmission bandwidth, andalso reduces the loss of detail information of region of interest. The paper mainlystudies on ROI compression algorithm based on CCSDS, and tries to detect theregions of interest of remote sensing images with itti’s model, which is one of themodels based on visual attention mechanism.Itti′s model is applied to detect the ship targets, which is considered as theregion of interest of ocean surveillance satellite images. It illustrates the algorithmprocess of Itti′s model: firstly, the saliency map is obtained with the fusion of remote sensing image features, such as colors, intensity, orientations and so on; Secondly,the focus of attention is extracted using the mechanism of winner-take-all andinhibition of return; finally, setting the focus of attention as the center, a circularsalient region with a fixed radius is obtained. The paper introduces a capacitor arraycharging model to describe the extracting and transferring process of the focus ofattention, and also introduces the discrete moment transform to enhance the responseof image texture features. Then, the threshold segmentation method is chosen toextract the salient region with the focus of attention. it is verified that both the shapeand size of the salient region are consistent well with the ship targets; thebackground contained in the salient region is also reduced significantly. Moreover,the improved algorithm has a good real-time performance. It comes to the conclusionthat compared with Itti′s model, the improved algorithm is more effective andsuitable for the extraction of ship targets detection of ocean satellite images.This paper introduces SPIHT, JPEG2000, CCSDS and so on. CCSDS dividesthe image into several segments, and each segment is coded independently. Differentsegments contain different information, equaling to the image texture complexity,which is measured by gradient in the paper. According to the value of the gradient,the paper allocates the rate in the way that the bigger the value of a segment gradientis, the more rate of the segment. The experiment shows that, the rate-allocatingalgorithm is beneficial to optimize the rate distortion performance of CCSDS.According to the characteristics of CCSDS, this paper presents a new ROIcompression algorithm. After segmented, the region of interest and background ofthe image are compressed independently in the algorithm, which is implementedfollowing the steps: firstly, the ROI mask is coded; secondly, the rate is allocatedinto the regions of interest and background based on the value of each region; thirdly,the regions of interest and background are coded. The experiment shows that, thealgorithm can improve the recovery effect of the regions of interest.