A Large Aperture Static Interference Hyperspectral Imaging Data Compression Method

After spectral reconstruction of large aperture static interferometry remote sensing data, a spectral image data cube can be generated that contains both spatial information about the ground objects and interference information. Considering the large volume of large aperture static interferometry remote sensing data and the scarce bandwidth of space-to-earth links, it is necessary to find suitable compression methods to compress this data. Starting from the mechanism of large aperture static interferometry imaging, based on the principles of large aperture static interferometry spectral imaging and the redundant information in the data, a compression algorithm called Spectral-Interference-Optical Path Difference Redundancy Removal (SIORR) is proposed. This algorithm fully considers the similarities between the interference curves of similar ground points and the redundancy between multiple frames. The SIORR algorithm can be divided into three parts. First, it analyzes and processes the interference curves in the hyperspectral data. In large aperture static interferometry spectral imaging remote sensing images, due to the continuity of spatial distribution of adjacent ground objects, the differences between interference curves of the same category are small. By constructing a table of typical interference curves to encode representations of different categories of interference curves, indexes of matching items and necessary correction information are recorded. Each table item not only represents a specific interference curve but also serves as a reference for compressing that type of curve. During the actual compression process, each interference curve in the original data is matched with an item in the curve table, and data compression and recovery are achieved by recording the index of the matching item and necessary correction information. Subsequently, during the interferometric imaging process, there is a high similarity between different optical path difference images, specifically reflected in the texture features of the remote sensing images. By using a prediction method to remove inter-frame correlations and utilizing the high correlation between different optical path difference images, while also avoiding the decrease in correlation caused by large differences in optical path difference, this algorithm adopts a grouping strategy. Every ten different optical path difference images are grouped together, and one is selected as the reference frame. Based on this reference frame, the other nine images are predicted. After these two processing steps, the correlation between different optical path difference images in large aperture static interferometry spectral imaging data has been reduced to about 0.5, while effectively reducing the quantization bit rate of pixel data points. After processing, the main information is stored in the image residuals and curve table suitable for compression, and the errors introduced by lossy compression are relatively small, thus the interference curves restored by the spectral curves are also closer to the original spectral curves. In lossy compression, spectral data is protected. Finally, the JPEG2000 image compression algorithm is used for lossless or lossy compression. Experimental results show that for large aperture static interferometry data, the proposed SIORR algorithm can achieve a 3.1x compression ratio in lossless compression. In lossy compression, the average peak signal-to-noise ratio is about 3 dB higher than that of other comparative algorithms. The spectral angle and relative quadratic error of the spectral curves of images restored by the SIORR algorithm are better than those processed by other comparison algorithms. The remote sensing images restored by the SIORR algorithm are also better than those of other comparison algorithms. Under lossless compression conditions, the SIORR algorithm can effectively increase the compression ratio. In lossy compression, compared to other algorithms, the SIORR algorithm has a higher image peak signal-to-noise ratio, and the interference curves and spectral curves are closer to the original curves, effectively protecting the spectral information. The SIORR algorithm not only has better compression effects but also has lower complexity and is easier to port, making it more suitable for compression processing of large aperture static interferometry remote sensing images.