Research On Theory And Methods Of Compressed Sensing For High Resolution Remote Sensing Optical Imaging

Spaceborne remote sensing imaging systems call for more stringent requirement ofthe imaging resolution. High resolution imaging systems require larger pixel arrays andsmaller pixel-pitch, the resulting huge amount of data also cause considerable burdenwith regard to data storage and real-time transmission. Compressed sensing (CS) breaksShannon/Nyquist sampling theorem bottleneck. It captures and represents signals at asampling rate significantly below the Nyquist rate, and then original signals can beaccurately or high precisely recovered by solving sparse optimization problems basedon signal sparsity or compressibility. This emerging signal acquisition theory provides anew method for designing high-resolution imaging systems.In this paper, we mainly research on the basis of the CS theory and its applicationfor high-resolution remote sensing imaging systems.In theoretical aspect, we have studied three basic theoretical problems.①Theimage sparse representation based on complete basis and over-complete dictionary areanalyzed. The K-SVD dictionary training and update methods based on sample imagesare also researched. These researches pave the way for its application on imagingsystems.②After an analysis of construction methods and recovery performances ofthe common measurement matrices. We put forward a method for constructingorthogonal sysmmetric circulant matrices (OSCM) and block circulant matrices (BCM).The optimization of OSCM is conducted by using Golay complementary. Thesimulations validate the benign reconstruction performance of the two measurementmatrices. On this basis, the hardware implementation of constructing cyclemeasurement matrices is studied, and the FPGA simulations validate the feasibility andefficiency of construction process. These works provide a theoretical support of signalacquirement in compressive imaging system.③Two kinds of image sparsereconstruction algorithms are studied, and their performance are contrastively analyzedthrough simulation experiments on two-dimensional images. Convex optimizationalgorithms are slower but more accurate than pursuit algorithms for two-dimensionalimage reconstruction. Therefore, we studied for improvement of gradient projection forsparse reconstruction (GPSR). The strategy is that the constraint parameter and thetermination threshold are selected differently at various iteration stages. Theseparameter steps are according to logarithmic scale. This strategy ensures that thealgorithm converges fast at the early stages and performs high precision at the laterstages. The research on reconstruction algorithms guarantees imaging quality incompressive imaging systems.As for application aspect, we have projected two high resolution imaging systems. ①We proposed a high resolution infrared imaging method based on frequency domainmodulation and coded aperture mask. For the frequency domain modulation method, theFourier transform and inverse Fourier transform of images are implemented by opticallenses. The light field after Fourier transform is modulated, and then Compressiveobservations are obtained by downsampling. For coded aperture method in spacedomain, a coded aperture mask is placed on the focal plane in the optical system to codeand capture the light field. The two approaches are validated by numerical experimentsand a comparative analysis is given out.②We researched a CMOS compressiveimaging method based on CS. On the basic property of two-dimensional separabletransform of images, the compressive samples are obtained by designing suitableperipheral circuit for the sensor. The DCT measurement matrix is constructed and itsperformance is analyzed based on cumulative mutual coherence. The circuit designcomprises tow aspects: the row transforms of images are implemented by the currentweighting summation of each column and the column transforms are performed byvector–matrix multiplier (VMM). All these operate in current domain. Experimentalresults demonstrate the effectiveness of the proposed method which reduces the datarate and thus lowers the pressure of digital-to-analog conversion, data storage andtransmission.In this paper, the theory and application of CS for high-resolution remote sensingoptical imaging have been researched. The work makes a contribution on furtherdevelopment and improvement the CS theory. The proposed application methods havebeen verified by some experiments, which provide a novel approach for designing acompressed imaging system.