Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation

	Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation
	Lu, Hongyang 1; Wei, Jingbo 1; Wang, Lizhe 1; Liu, Peng 1; Liu, Qiegen 1; Wang, Yuhao 1; Deng, Xiaohua 1
刊名	Remote Sensing
	2016
卷号	8 期号:6
关键词	TEXTURE ANALYSIS SAR IMAGES RANDOM FOREST CLASSIFICATION FEATURES
通讯作者	Wang, Lizhe (lizhe.wang@gmail.com)
英文摘要	Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in remote sensing applications. Recent studies have demonstrated that reference image-based reconstruction methods have great potential for higher reconstruction performance, while lacking accuracy and quality of reconstruction. For this application, a new compressed sensing objective function incorporating a reference image as prior information is developed. We resort to the reference prior information inherent in interior and exterior data simultaneously to build a new generalized nonconvex low-rank approximation framework for RSI reconstruction. Specifically, the innovation of this paper consists of the following three respects: (1) we propose a nonconvex low-rank approximation for reconstructing RSI; (2) we inject reference prior information to overcome over smoothed edges and texture detail losses; (3) on this basis, we combine conjugate gradient algorithms and a single-value threshold (SVT) simultaneously to solve the proposed algorithm. The performance of the algorithm is evaluated both qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm improves several dBs in terms of peak signal to noise ratio (PSNR) and preserves image details significantly compared to most of the current approaches without reference images as priors. In addition, the generalized nonconvex low-rank approximation of our approach is naturally robust to noise, and therefore, the proposed algorithm can handle low resolution with noisy inputs in a more unified framework.
学科主题	Remote Sensing
类目[WOS]	Remote Sensing
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20162502527038
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39497]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Department of Electronic Information Engineering, Nanchang University, Nanchang 2.330031, China 3. Institute of Space Science and Technology, Nanchang University, Nanchang 4.330031, China 5. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 6.100094, China 7. School of Computer Science, China University of Geosciences, Wuhan 8.430074, China
推荐引用方式 GB/T 7714	Lu, Hongyang,Wei, Jingbo,Wang, Lizhe,et al. Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation[J]. Remote Sensing,2016,8(6).
APA	Lu, Hongyang.,Wei, Jingbo.,Wang, Lizhe.,Liu, Peng.,Liu, Qiegen.,...&Deng, Xiaohua.(2016).Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation.Remote Sensing,8(6).
MLA	Lu, Hongyang,et al."Reference information based remote sensing image reconstruction with generalized nonconvex low-rank approximation".Remote Sensing 8.6(2016).