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Quality Improvement of Remote Sensing Images

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 22327

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Special Issue Editors

Dr. Yi Guo

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Centre for Research in Mathematics and Data Science, Western Sydney University, Parramatta, NSW 2150, Australia
Interests: computational statistics; data science; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lifu Zhang

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; data fusion; quality enhancement
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Prof. Feng Li

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Chinese Academy of Space Technology, China
Interests: super resolution; remote sensing image processing; machine learning; compressive sensing

Dr. Laurence Park

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Western Sydney University, Victoria Road, Parramatta, 2051, NSW, Australia
Interests: machine learning; data mining; knowledge discovery; statistics

Special Issue Information

Dear Colleagues,

As the demand of large-scale Earth observation in a great deal of scientific research increases exponentially, the main data acquisition technique—remotely sensed imagery—has been developing rapidly to produce high-quality images in recent years to meet the needs of critical applications such as food security and climate change research.

However, due to the bottlenecks originating from sensor physics, manufacturing processes, energy consumption, and even operational strategies, high signal-to-noise ratio and resolutions in spectral, spatial, and temporal dimensions (i.e., the main indicators of quality) cannot be achieved at the same time. This limits the application of remote sensing imagery in areas such as crop monitoring because the objects of interest are simply not visible at the available resolution.

There are tremendous efforts in the remote sensing community, in both sensing mechanics/physics and data analysis, working towards a breakthrough of this problem. For resolution augmentation, many effective approaches have been proposed in super-resolution, spatial temporal data fusion, spectral bands enhancement, heterogeneous/multiple source remote sensing data fusion, and so on, aiming at increasing the resolution in one or more dimensions of the original images. This is unique in remote sensing because of the versatility of the data sources in this domain. Quality enhancement is a fundamental and vibrant research problem in the image processing community. Interestingly, the richness in the spectral dimension of remote sensing imagery, specifically considering multiple/hyperspectral images, opens up opportunities for more models and methods than traditional image processing. Examples include spectral/spatial noise estimation and reduction, intrinsic dimensionality estimation, spectral data subspace/manifold learning, and many more. The research on this topic has accumulated momentum in recent years.

To accelerate it even further, we propose this Special Issue in MDPI’s journal Remote Sensing, seeking novel solutions to the improvement of remote sensing image quality, accepting papers on subjects ranging from sensing techniques to data analysis and from mathematical/statistical modelling to machine learning. We also welcome application papers of enhanced remote sensing images, as a completion of this research loop. This Special Issue serves as a firm stepping stone for modelers and practitioners for remote sensing image quality improvement.

Dr. Yi Guo
Prof. Lifu Zhang
Prof. Feng Li
Dr. Laurence Park
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

quality enhancement
data fusion
super-resolution
machine learning
spectral data analysis
noise estimation
manifold learning
subspace clustering

Published Papers (7 papers)

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23 pages, 8581 KiB

Open AccessArticle

An Improved Cloud Detection Method for GF-4 Imagery

by Ming Lu, Feng Li, Bangcheng Zhan, He Li, Xue Yang, Xiaotian Lu and Huachao Xiao

Remote Sens. 2020, 12(9), 1525; https://doi.org/10.3390/rs12091525 - 11 May 2020

Cited by 9 | Viewed by 2942

Abstract

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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21 pages, 4665 KiB

Open AccessArticle

Radiometric Cross-Calibration of the Wide Field View Camera Onboard GaoFen-6 in Multispectral Bands

by Aixia Yang, Bo Zhong, Longfei Hu, Shanlong Wu, Zhaopeng Xu, Hongbo Wu, Junjun Wu, Xueshuang Gong, Haibo Wang and Qinhuo Liu

Remote Sens. 2020, 12(6), 1037; https://doi.org/10.3390/rs12061037 - 24 Mar 2020

Cited by 28 | Viewed by 3811

Abstract

GaoFen6 (GF-6), successfully launched on June 2, 2018, is the sixth satellite of the High-Definition Earth observation system (HDEOS). Although GF-6 is the first high-resolution satellite in China to achieve precise agricultural observation, it will be widely used in many other domains, such as land survey, natural resources management, emergency management, ecological environment and so on. The GF-6 was not equipped with an onboard calibration instrument, so on-orbit radiometric calibration is essential. This paper aimed at the on-orbit radiometric calibration of the wide field of view camera (WFV) onboard GF-6 (GF-6/WFV) in multispectral bands. Firstly, the radiometric capability of GF-6/WFV is evaluated compared with the Operational Land Imager (OLI) onboard Landsat-8, Multi Spectral Instrument (MSI) onboard Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra, which shows that GF-6/WFV has an obvious attenuation. Consequently, instead of vicarious calibration once a year, more frequent calibration is required to guarantee its radiometric consistency. The cross-calibration method based on the Badain Jaran Desert site using the bi-directional reflectance distribution function (BRDF) model calculated by Landsat-8/OLI and ZY-3/Three-Line Camera (TLC) data is subsequently applied to GF-6/WFV and much higher frequencies of calibration are achieved. Finally, the cross-calibration results are validated using the synchronized ground measurements at Dunhuang test site and the uncertainty of the proposed method is analyzed. The validation shows that the relative difference of cross-calibration is less than 5% and it is satisfied with the requirements of cross-calibration. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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23 pages, 8257 KiB

Open AccessArticle

An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites

by Xue Yang, Feng Li, Lei Xin, Xiaotian Lu, Ming Lu and Nan Zhang

Remote Sens. 2020, 12(3), 466; https://doi.org/10.3390/rs12030466 - 2 Feb 2020

Cited by 8 | Viewed by 2940

Abstract

Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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16 pages, 6529 KiB

Open AccessArticle

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

by Luxiao He, Mi Wang, Xueli Chang, Zhiqi Zhang and Xiaoxiao Feng

Remote Sens. 2019, 11(21), 2472; https://doi.org/10.3390/rs11212472 - 23 Oct 2019

Cited by 1 | Viewed by 2174

Abstract

Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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19 pages, 5202 KiB

Open AccessArticle

Fusion of Various Band Selection Methods for Hyperspectral Imagery

by Yulei Wang, Lin Wang, Hongye Xie and Chein-I Chang

Remote Sens. 2019, 11(18), 2125; https://doi.org/10.3390/rs11182125 - 12 Sep 2019

Cited by 12 | Viewed by 2448

Abstract

This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, n_BS. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same n_BS. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also selected by other BS methods, in this case each band in JBS is prioritized by the frequency of the band appearing in the band subsets to be fused. Such frequency is then used to calculate the priority probability of this particular band in the JBS. Because the JBS is obtained by taking the union of all band subsets, the number of bands in the JBS is at least equal to or greater than n_BS. So, there may be more than n_BS bands, in which case, BSF uses the frequency-calculated priority probabilities to select n_BS bands from JBS. Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose. Of particular interest is that BSF can prioritize bands without band de-correlation, which has been a major issue in many BS methods using band prioritization as a criterion to select bands. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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12 pages, 8738 KiB

Open AccessTechnical Note

Prediction of High-Quality MODIS-NPP Product Data

by Zhenhua Liu, Ting Wang, Yonghua Qu, Huiming Liu, Xiaofang Wu and Ya Wen

Remote Sens. 2019, 11(12), 1458; https://doi.org/10.3390/rs11121458 - 20 Jun 2019

Cited by 6 | Viewed by 3250

Abstract

Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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13 pages, 4299 KiB

Open AccessLetter

Hyperspectral and Multispectral Image Fusion Using Cluster-Based Multi-Branch BP Neural Networks

by Xiaolin Han, Jing Yu, Jiqiang Luo and Weidong Sun

Remote Sens. 2019, 11(10), 1173; https://doi.org/10.3390/rs11101173 - 16 May 2019

Cited by 34 | Viewed by 3629

Abstract

Fusion of the high-spatial-resolution hyperspectral (HHS) image using low-spatial- resolution hyperspectral (LHS) and high-spatial-resolution multispectral (HMS) image is usually formulated as a spatial super-resolution problem of LHS image with the help of an HMS image, and that may result in the loss of detailed structural information. Facing the above problem, the fusion of HMS with LHS image is formulated as a nonlinear spectral mapping from an HMS to HHS image with the help of an LHS image, and a novel cluster-based fusion method using multi-branch BP neural networks (named CF-BPNNs) is proposed, to ensure a more reasonable spectral mapping for each cluster. In the training stage, considering the intrinsic characteristics that the spectra are more similar within each cluster than that between clusters and so do the corresponding spectral mapping, an unsupervised clustering is used to divide the spectra of the down-sampled HMS image (marked as LMS) into several clusters according to spectral correlation. Then, the spectrum-pairs from the clustered LMS image and the corresponding LHS image are used to train multi-branch BP neural networks (BPNNs), to establish the nonlinear spectral mapping for each cluster. In the fusion stage, a supervised clustering is used to group the spectra of HMS image into the clusters determined during the training stage, and the final HHS image is reconstructed from the clustered HMS image using the trained multi-branch BPNNs accordingly. Comparison results with the related state-of-the-art methods demonstrate that our proposed method achieves a better fusion quality both in spatial and spectral domains. Full article

(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)

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