LOSSLESS INTER-ARRAY PREDICTIVE CODING FOR SUBPIXEL-SHIFTED SATELLITE IMAGES BASED ON TEXTURE ANALYSIS

Two adaptive inter-array predictive coding methods based on texture analysis for subpixelshifted super-resolution imaging are presented. The technique of subpixel-shifted superresolution imaging is outstanding in improving the spatial resolution of satellite images effectively, without greatly reducing the imaging swath or increasing the mass of the sensors. However, the technique leads to marked proliferation of transferring data with the resolution enhancement, so an efficient lossless data compression algorithm calls for special study. This paper analyses the particular spatial distribution relationship of subpixel-shifted inter-array pixels and proposes new lossless predictive compression adaptive to the local texture feature. Performance comparisons are made between the new methods and the traditional linear predictors, and also between these two new methods. Experimental results show that the proposed methods perform excellently in adaptability and compression efficiency for high resolution satellite images rich in textures. INTRODUCTION High-resolution satellite imaging aims at observing ground targets in great details and at a large range. With the progress of spatial techniques, modern high resolution satellites with a swath of hundreds of kilometers and a submeter spatial resolution are gradually coming into use. Unfortunately, within the conventional optical imaging schemes, if the spatial resolution is improved by twice, the swath width will suffer a reduction by half and the mass of on-board sensors will be increased by an order of magnitude. Spatial resolution, imaging swath and sensor weight together constitute three fundamental and incompatible qualifications. Conventional data compression techniques difficultly meet at the mean time the specifications of compression ratio, computing efficiency and data integrity of the target areas. In order to ensure enhancing the spatial resolution effectively without reducing its observation width or increasing the sensor mass markedly, the technique of synthesizing a higher resolution image from several comparative lower resolution images is receiving growing attention in related areas (Simon and Takeo, 2002). The 2002 launched French satellite SPOT 5 first successfully yielded a single 2.5 m resolution image from two 5m resolution images acquired simultaneously in its HRG panchromatic band. Since collected data rises greatly to 128 Mbps, compared with 32 Mbps for its predecessors, dedicated data flow control is introduced so that each of the two 5 m resolution CCD images is separately compressed by lossy variable DCT and synthesized into a 2.5 m resolution image on the ground (www.spotimage.fr). In order to further lessen the downlink data at no loss, some Gao S. , Zhang X.J. and Sun W.D. 276 specific compression algorithms are desiderated for the subpixel-shifted super-resolution imaging. Seeing that corresponding detectors of the two CCD sensors are vertically and horizontally offset by only half a pixel, much data redundancy exists based on the grey continuity of ground objects. In this paper, an adaptive lossless inter-array predictive coding based on texture analysis is proposed, specific to this subpixel-shifted super-resolution imaging. Firstly 2-level integer wavelet transform is applied to the two subpixel-shifted images; then, inter-array prediction for the second image is made from the first one using texture analysis; finally the entropy coding is carried for the prediction error and the lossless compression is achieved. Experimental results show that, compared with the traditional linear prediction, the texture-based adaptive inter-array predictive algorithm applies well to high resolution satellite images rich in textures, allowing great improvement in terms of compression ratio and signal/noise ratio. PRINCIPLES OF SUBPIXEL-SHIFTED SUPER-RESOLUTION IMAGING In subpixel-shifted super-resolution imaging, several identical CCD arrays with nonintegral pixels offset along X-axis and Y-axis in the focal plane are simultaneously pushbrooming. This is a process of separate sampling and quantifying that produces several images with interrelated but not same information of the ground targets. Without variating the size or mass of the CCD sensors or the condition of optical systems, these images are integrated utilizing the correlation consisting in the spatially overlapped images. This approach actually reduces the spatial sampling interval, through neutralizing the effect of lack sampling and degeneration. In the case of subpixel-shifted twin-CCD observation, the two arrays are offset by 1/2 pixel along the array-direction (the X-axis), and by N+1/2 pixels along the flight-direction (the Y-axis) where N is the least possible positive integer, forming a spatial arrangement as showed in Figure 1a, and produce two images stagger 1/2 pixel away along X-axis and Yaxis respectively. If the two images are overlapped based on the corresponding geographic position they are recording, the sampling centre of each pixel in the second image lies at the very low-right corner of the corresponding pixel in the first image (see Figure 1b). Theoretical calculation and experimental results indicate that, if making good use of the inter-array correlation, the subpixel-shifted super-resolution imaging technique is able to improve the spatial resolution to be 1.7~1.8 times that a single CCD image could offer (Gong et al., 2002).