Research On The Acquisition Of Compressive Sensing Signal For Micro-nano Satellite

With the development of imaging technique and electronic devices,the novel camera sensor helps us to obtain remote sensing image with higher resolution.Whereas large amount of data is one of the most obvious features in satellite based remote sensing systems,which is also a burden for data processing and transmission on Micro/Nano-Satellite.In order to solve this problem,in this paper,a remote sensing images acquisition framework based on the theory of compressive sensing(CS)is proposed for Micro/Nano-Satellite,which combines the procedures of sampling and compression so that compressive sensing based measurements are already compressed version of the original signal.Therefore,the compression unit is no longer necessary in our method,which greatly reduces the pressure on the encoder.The whole framework is able to provide a quick view without any iterative computational efforts.A reconstruction algorithm based on the proposed image acquisition framework is also designed in this paper.From the experimental results,it can be seen that our algorithm performs better both in terms of reconstruction quality and computational efficiency than other traditional algorithms.Meanwhile,the proposed framework can be applied to high frame rate cameras for its high-efficiency sampling.Since High frame rate cameras are of high frame frequency output and large data capacity,it’s a huge burden for data transmission and storage,which also leads to great power consumption.The main contributions of this paper are summarized as follows:1.We propose a novel image acquisition framework for Micro/Nano-Satellite based optical Earth observation applications.Unlike conventional image acquisition methods apply complex calculation in encoder side,the proposed framework moves these computational efforts to decoder side,which is more suitable for Micro/Nano-Satellite.2.We come up with a reconstruction model based on the proposed framework.A reconstruction algorithm is designed to solve this model by utilizing Split Bregman iteration,which outperforms other compressive sensing algorithms.3.This paper illustrates the implementation of proposed framework,and experimental results show that our method performs much better than conventional compressive sensing methods in reconstructed image quality.