Compressed sensing (CS) is a new signal processing theory that provides an insight into signal processing. The CS theory has numerous potential applications in various fields, such as image processing, astronomical data analysis, analog-to-information, medical imaging, and remote sensing (RS) imagery. The CS theory is applied to RS video imagery. An RS video based on a compressed sensing (RS-VCS) framework with correlation estimation measurement is proposed, along with a block measurement correlation model and corresponding reconstruction. The linearized Bregman algorithm is used to solve the reconstruction model, and the performance of the RS-VCS framework is simulated numerically.
Most recently, an emerging Compressed Sensing (CS) theory has brought a major breakthrough for data acquisition and recovery. It asserts that a signal, which is highly compressible in a known basis, can be reconstructed with high probability through sampling frequency which is well below Nyquist Sampling Frequency. When applying CS to Remote Sensing (RS) Video imaging, it can directly and efficiently acquire compressed image data by randomly projecting original data to obtain linear and non-adaptive measurements. In this paper, with the help of distributed video coding scheme which is a low-complexity technique for resource limited sensors, the frames of a RS video sequence are divided into Key frames (K frames) and Non-Key frames (CS frames). In other words, the input video sequence consists of many groups of pictures (GOPs) and each GOP consists of one K frame followed by several CS frames. Both of them are measured based on block, but at different sampling rates. In this way, the major encoding computation burden will be shifted to the decoder. At the decoder, the Side Information (SI) is generated for the CS frames using traditional Motion-Compensated Interpolation (MCI) technique according to the reconstructed key frames. The over-complete dictionary is trained by dictionary learning methods based on SI. These learning methods include ICA-like, PCA, K-SVD, MOD, etc. Using these dictionaries, the CS frames could be reconstructed according to sparse-land model. In the numerical experiments, the reconstruction performance of ICA algorithm, which is often evaluated by Peak Signal-to-Noise Ratio (PSNR), has been made compared with other online sparse representation algorithms. The simulation results show its advantages in reducing reconstruction time and robustness in reconstruction performance when applying ICA algorithm to remote sensing video reconstruction.
Compressed sensing (CS) breaks Shannon/Nyquist sampling theorem bottleneck. It captures and represents signals at a sampling rate significantly below the Nyquist rate, and then original signals can be accurately or high precisely recovered by solving sparse optimization problems based on signal sparsity or compressibility. CS has a good application prospect in remote sensing imagery, especially in Infrared Remote Sensing Video. The CS-based remote sensing includes two stages: onboard encoding imaging and offline decoding recovery. Video offline decoding recovery is one of the core questions in CS-Based Infrared Remote Sensing Video systems. In this paper, firstly, we introduce a coupled optimization models which is composed of a single model and an error model for video offline decoding recovery. This paper shows that the coupled models can easily improve speed and accuracy to recover video of Infrared Remote Sensing. Secondly, we review the Bregman method and linearized Bregman method. Furthermore, a linearized Bregman error iteration algorithm is proposed for solving the coupled models, thus lead to better convergence rates and error performances. In numerical experiments, we compare the convergence rates of the original Bregman method and the linearized Bregman method from the first frame picture, and test the performance of the linearized Bregman method for video recovery with the single model and coupled models. Numerical experiments demonstrate the effectiveness of the proposed algorithm. The comparison with the single model is included.