CAJ | 학술논문

针对卫星云图数据量大，但传输通道和存储空间相对狭小的问题，本文提出了一种基于 Tetrolet 变换的卫星云图分块压缩感知方法。该方法将 Tetrolet 变换引入压缩感知的稀疏表示环节，以刻画卫星云图细节丰富，纹理复杂的特性，而且将分块压缩感知与平滑投影Landweber迭代方法结合用于云图重构，以提高计算效率。同时，为了进一步提高重构云图的质量，本文对云图的稀疏表示提出了另一种改进方案，首先对原始云图进行拉普拉斯金字塔分解，将得到的低频分量和高频分量分别进行分块及采样，并对低频及高频分量分别进行离散小波变换(DWT)及Tetrolet变换以实现稀疏表示，此不仅可以发挥不同稀疏变换各自的优点，而且充分利用了Tetrolet变换在表示云图方向纹理和边缘等重要信息方面的优势。实验结果表明，在相同采样率下，本文方法的重构结果明显优于直接用Tetrolet，DWT，Contourlet和DCT变换对卫星云图进行稀疏表示的重构结果。
침대위성운도수거량대，단전수통도화존저공간상대협소적문제，본문제출료일충기우 Tetrolet 변환적위성운도분괴압축감지방법。해방법장 Tetrolet 변환인입압축감지적희소표시배절，이각화위성운도세절봉부，문리복잡적특성，이차장분괴압축감지여평활투영Landweber질대방법결합용우운도중구，이제고계산효솔。동시，위료진일보제고중구운도적질량，본문대운도적희소표시제출료령일충개진방안，수선대원시운도진행랍보랍사금자탑분해，장득도적저빈분량화고빈분량분별진행분괴급채양，병대저빈급고빈분량분별진행리산소파변환(DWT)급Tetrolet변환이실현희소표시，차불부가이발휘불동희소변환각자적우점，이차충분이용료Tetrolet변환재표시운도방향문리화변연등중요신식방면적우세。실험결과표명，재상동채양솔하，본문방법적중구결과명현우우직접용Tetrolet，DWT，Contourlet화DCT변환대위성운도진행희소표시적중구결과。
Due to the difficulties caused by large satellite cloud image data with limited transmission channel and storage space, an approach of block compressed sensing of satellite cloud images is proposed based on Tetrolet transform. This approach introduces Tetrolet transform into the sparse representation step of compressed sensing which can depict the detail and texture structure of satellite cloud image well, and combines block compressed sensing with smooth projection Landweber iteration method to accomplish image reconstruction which can improve the computational efficiency. Meanwhile, in order to further improve the quality of reconstructed cloud images, another improvement scheme for the sparse representation of cloud images is proposed. Firstly, a layer of Laplacian pyramid decomposition of the original image is taken, and the low frequency component and high frequency component obtained are divided into blocks and sampled respectively. Then, the low frequency component is represented by Wavelet transform, while the high frequency component is represented by Tetrolet transform, which can not only play the advantage of different sparse representation, but also make full use of the advantages of Tetrolet transform in expressing the important information of cloud images, such as directional texture and edge information. The experimental results show that the reconstruction quality of the proposed method is obviously superior to Tetrolet, DWT, Contourlet and DCT sparse representation methods under the same sampling rate.