Super-Resolution Based on Compressive Sensing and Structural Self-Similarity for Remote Sensing Images

Pan, Zongxu; Yu, Jing; Huang, Huijuan; Hu, Shaoxing; Zhang, Aiwu; Ma, Hongbing; Sun, Weidong

doi:10.1109/tgrs.2012.2230270

Cited by 123 publications

(55 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in representation of lakes using data time series, halftoning, and morphological filtering (Muad and Foody 2012). Approaches based on discrete wavelet transform for hyperspectral images (Patel and Joshi 2015) and structural self-similarity, identifying similar structures in RS images (Pan et al 2013), have been recently proposed. Assuming the existence of co-registered images of different spatial and spectral resolutions, Atkinson et al (2008) used downscaling cokriging for image mapping, whereas Song et al (2015) avoided the restriction for co-registration through an image degradation model via blurring and downsampling and deriving a simulated medium resolution image from a high resolution one; the former was then used to extract a dictionary employed to increase the resolution of the originally targeted medium resolution image.…”

Section: Aquatic Mappingmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

View full text Add to dashboard Cite

Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

show abstract

Section: Aquatic Mappingmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

View full text Add to dashboard Cite

show abstract

“…Boucher et al [2] also proposed a geostatistical solution for super-resolution of remote sensing images using sequential indicator simulation to obtain multiple possible stochastic results. Pan et al [7] proposed a super-resolution reconstruction method of remote sensing images based on compressive sensing, structural self-similarity, and dictionary learning. Generally speaking, super-resolution reconstruction is a process of combining one or multiple low-resolution spatial images to produce a high-resolution image, and is a powerful tool to acquire the desired spatial resolution in remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping

Zhang

2017

Remote Sensing

View full text Add to dashboard Cite

When using coarse-resolution remote sensing images, super-resolution reconstruction is widely desired, and can be realized by reproducing the intrinsic features from a set of coarse-resolution fraction data to fine-resolution remote sensing images that are consistent with the coarse fraction information. Prior models of spatial structures that encode the expected features at the fine (target) resolution are helpful to constrain the spatial patterns of remote sensing images to be generated at that resolution. These prior models can be used properly by multiple-point statistics (MPS), capable of extracting the intrinsic features of patterns from prior models such as training images, and copying them to the simulated regions using hard and soft conditional data, or even without any conditional data. However, because traditional MPS methods based on linear dimensionality reduction are not suitable to deal with nonlinear data, and isometric mapping (ISOMAP) can reduce the dimensionality of nonlinear data effectively, this paper presents a sequential simulation framework for generating super-resolution remote sensing images using ISOMAP and MPS. Using four different examples, it is demonstrated that the structural characteristics of super-resolution reconstruction of remote sensing images using this method, are similar to those of training images.

show abstract

“…Example-based SR approaches break the limitations existing in the traditional reconstruction-based algorithms. They learn the mapping relationship between the corresponding pre-processed low and high resolution training samples to recover the missed HF details, mainly including learningbased approach [4], neighborhood embedding approach [5] and sparse representation methods [6][7][8][9][10][11][12][13][14][15][16][17][18], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The reconstruction method in [17] modified the algorithm of Yang [6][7][8] with the CS theory, reducing the number of training dictionary but still using external several training images. Pan in [15][16] and Zhu in [18] modified the method in [17] not relying on any external images, assuming that the low resolution patches can be considered as the compressed sensing version of high resolution patches under the framework of CS theory.…”

Section: Introductionmentioning

confidence: 99%

Image Super-resolution Reconstruction Algorithm Based on Self-Similarity and Compressive Sensing

Yao¹,

Xie²

2017

IJSIP

View full text Add to dashboard Cite

show abstract

Super-Resolution Based on Compressive Sensing and Structural Self-Similarity for Remote Sensing Images

Cited by 123 publications

References 35 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping

Image Super-resolution Reconstruction Algorithm Based on Self-Similarity and Compressive Sensing

Contact Info

Product

Resources

About