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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 (31 October 2020) | Viewed by 53481

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Dr. Massimo Selva

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Institute of Applied Physics ”Nello Carrara”, National Research Council, Via Madonna del Piano, 10, 50019 Sesto Fiorentino, FI, Italy
Interests: image and signal processing; image quality; image compression; hyperspectral and multispectral image processing and analysis; color management; restoration; image noise estimation; MTF estimation; data fusion; pansharpening; hypersharpening

Special Issue Information

Dear Colleagues,

The need of observing and characterizing the environment leads to constant increase of the spatial, spectral and radiometric resolution of the new optical sensors. Recently, due to the commissioning of constellation of satellites, also the revisiting time of the sites is reducing so that multi-temporal analysis is becoming widespread. Furthermore, the availability of many acquisition systems opens the way to multisensors analysis.

The key idea behind this special issue is presenting the latest research results and outcomes about processing of optical remote sensing data embracing all the specific topics that impact on the quality of the data.

Remote sensing images, in fact, are acquired to satisfy the needs of the users. In this perspective, the quality of the images is the degree to which the set of their characteristics fulfils those needs. Clearly, the quality of the images provided to users does not only depends on the characteristics of the data acquired but also on the chain that processes the images. Each algorithm and methodology of the processing chain has an impact on the quality of the data; it can, in fact, preserve, improve or unfortunately degrade the quality of the acquisition.

The scope of this special issue considers not only the topics that usually deal with quality but methods that produce data having "more quality" for satisfying the users' needs. Therefore, this special issue regards such topics as atmospheric correction and data fusion that are usually not treated together.

The expected contributions also concerns innovative indexes to assess the quality of the images in relationship with the needs of specific users.

To sum up, this special issue takes an overall view on the workflow from the acquisition to the users. It welcomes contributions having the focus on the quality of the optical remote sensing data and includes, without being limited to, the following subjects:

*Lossy and lossless compression with focus on multispectral and hyperspectral data.

*Instrument characterization, data correction and validation of up-to-date optical sensors.

*Advanced methodologies for atmospheric correction.

*Geometric correction and co-registration for data acquired by innovative platform also including UAV.

* Advanced restoration methodologies based on blind and model-based approaches.

*Up-to-date denoising techniques based on specific noise modelling.

*Pansharpening and data fusion for multispectral and hyperspectral data

Dr. Massimo Selva
Guest Editor

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.

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Keywords

Acquisition system
Image processing
Image quality
Optical data
Remote sensing

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Published Papers (13 papers)

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Editorial

Jump to: Research

4 pages, 665 KiB

Open AccessEditorial

The Quality of Remote Sensing Optical Images from Acquisition to Users

by Massimo Selva

Remote Sens. 2021, 13(7), 1295; https://doi.org/10.3390/rs13071295 - 29 Mar 2021

Cited by 1 | Viewed by 2110

Abstract

The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...] Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

Research

Jump to: Editorial

24 pages, 5464 KiB

Open AccessArticle

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

by Giruta Kazakeviciute-Januskeviciene, Edgaras Janusonis, Romualdas Bausys, Tadas Limba and Mindaugas Kiskis

Remote Sens. 2020, 12(24), 4152; https://doi.org/10.3390/rs12244152 - 18 Dec 2020

Cited by 13 | Viewed by 3098

Abstract

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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40 pages, 3189 KiB

Open AccessArticle

Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

by Sakib Kabir, Larry Leigh and Dennis Helder

Remote Sens. 2020, 12(24), 4029; https://doi.org/10.3390/rs12244029 - 9 Dec 2020

Cited by 30 | Viewed by 4904

Abstract

Over the past decade, number of optical Earth-observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lack an on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with their specifications or requirements. In cases of multiple methods, recommendations have been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that need to be assessed to evaluate remote sensing satellite data quality, and the best vicarious methodologies to evaluate identified quality parameters such as coherent noise and ground sample distance. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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31 pages, 10830 KiB

Open AccessArticle

Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images

by Oleg Ieremeiev, Vladimir Lukin, Krzysztof Okarma and Karen Egiazarian

Remote Sens. 2020, 12(15), 2349; https://doi.org/10.3390/rs12152349 - 22 Jul 2020

Cited by 28 | Viewed by 4194

Abstract

Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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14 pages, 14259 KiB

Open AccessArticle

Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)

by Cheng-Chien Liu

Remote Sens. 2020, 12(10), 1555; https://doi.org/10.3390/rs12101555 - 14 May 2020

Cited by 2 | Viewed by 3837

Abstract

The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes are usually included and presented in existing web mapping services. This work proposes a level-of-detail (LOD) based enhancement approach to present satellite imagery with an adaptively enhanced contrast determined by its viewing LOD. Compared to existing web mapping services, this new approach provides a better visual effect as well as spectral details of satellite imagery for cases partially covered with clouds or cirrocumulus clouds. The full archive of global satellite imagery, either the existing one or the one collected in the future, can be utilized and shared through the Web with the processing proposed in this new approach. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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27 pages, 8310 KiB

Open AccessArticle

Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

by Lei Sun, Bujin Li and Yongjian Nian

Remote Sens. 2020, 12(8), 1324; https://doi.org/10.3390/rs12081324 - 22 Apr 2020

Cited by 5 | Viewed by 2922

Abstract

HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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27 pages, 7982 KiB

Open AccessArticle

Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

by Deep Inamdar, Margaret Kalacska, George Leblanc and J. Pablo Arroyo-Mora

Remote Sens. 2020, 12(4), 641; https://doi.org/10.3390/rs12040641 - 14 Feb 2020

Cited by 23 | Viewed by 4311

Abstract

In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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25 pages, 21507 KiB

Open AccessFeature PaperArticle

Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs)

by Chiman Kwan, Bulent Ayhan, Jude Larkin, Liyun Kwan, Sergio Bernabé and Antonio Plaza

Remote Sens. 2019, 11(20), 2377; https://doi.org/10.3390/rs11202377 - 14 Oct 2019

Cited by 28 | Viewed by 3932

Abstract

We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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23 pages, 2333 KiB

Open AccessArticle

Fast Reproducible Pansharpening Based on Instrument and Acquisition Modeling: AWLP Revisited

by Gemine Vivone, Luciano Alparone, Andrea Garzelli and Simone Lolli

Remote Sens. 2019, 11(19), 2315; https://doi.org/10.3390/rs11192315 - 4 Oct 2019

Cited by 57 | Viewed by 3803

Abstract

Pansharpening is the process of merging the spectral resolution of a multi-band remote-sensing image with the spatial resolution of a co-registered single-band panchromatic observation of the same scene. Conceived and contextualized over 30 years ago, panharpening methods have progressively become more and more sophisticated, but simultaneously they have started producing fewer and fewer reproducible results. Their recent proliferation is most likely due to the lack of standardized assessment procedures and especially to the use of non-reproducible results for benchmarking. In this paper, we focus on the reproducibility of results and propose a modified version of the popular additive wavelet luminance proportional (AWLP) method, which exhibits all the features necessary to become the ideal benchmark for pansharpening: high performance, fast algorithm, absence of any manual optimization, reproducible results for any dataset and landscape, thanks to: (i) spatial analysis filter matching the modulation transfer function (MTF) of the instrument; (ii) spectral transformation implicitly accounting for the spectral responsivity functions (SRF) of the multispectral scanner; (iii) multiplicative detail-injection model with correction of the path-radiance term introduced by the atmosphere. The revisited AWLP has been comparatively evaluated with some of the high performing methods in the literature, on three different datasets from different instruments, with both full-scale and reduced-scale assessments, and achieves the first place, on average, in the ranking of methods providing reproducible results. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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22 pages, 5977 KiB

Open AccessArticle

A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

by Qi Zhang, Penglin Zhang and Yao Xiao

Remote Sens. 2019, 11(16), 1841; https://doi.org/10.3390/rs11161841 - 7 Aug 2019

Cited by 7 | Viewed by 3056

Abstract

The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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19 pages, 2871 KiB

Open AccessArticle

An Effectiveness Evaluation Model for Satellite Observation and Data-Downlink Scheduling Considering Weather Uncertainties

by Siyue Zhang, Yiyong Xiao, Pei Yang, Yinglai Liu, Wenbing Chang and Shenghan Zhou

Remote Sens. 2019, 11(13), 1621; https://doi.org/10.3390/rs11131621 - 8 Jul 2019

Cited by 16 | Viewed by 4665

Abstract

Low Earth orbit (LEO) satellites play an important role in human space activities, and market demands for commercial uses of LEO satellites have been increasing rapidly in recent years. LEO satellites mainly consist of Earth observation satellites (EOSs), the major commercial applications of which are various sorts of Earth observations, such as map making, crop growth assessment, and disaster surveillance. However, the success rates of observation tasks are influenced considerably by uncertainties in local weather conditions, inadequate sunlight, observation dip angle, and other practical factors. The available time windows (ATWs) suitable for observing given types of targets and for transmitting data back to ground receiver stations are relatively narrow. In order to utilize limited satellite resources efficiently and maximize their commercial benefits, it is necessary to evaluate the overall effectiveness of satellites and planned tasks considering various factors. In this paper, we propose a method for determining the ATWs considering the influence of sunlight angle, elevation angle, and the type of sensor equipped on the satellite. After that, we develop a satellite effectiveness evaluation (SEE) model for satellite observation and data-downlink scheduling (SODS) based on the Availability–Capacity–Profitability (ACP) framework, which is designed to evaluate the overall performance of satellites from the perspective of time resource utilization, the success rate of tasks, and profit return. The effects of weather uncertainties on the tasks’ success are considered in the SEE model, and the model can be applied to support the decision-makers on optimizing and improving task arrangements for EOSs. Finally, a case study is presented to demonstrate the effectiveness of the proposed method and verify the ACP-based SEE model. The obtained ATWs by the proposed method are compared with those by the Systems Tool Kit (STK), and the correctness of the method is thus validated. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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24 pages, 6985 KiB

Open AccessArticle

Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery

by Michal Kedzierski, Damian Wierzbicki, Aleksandra Sekrecka, Anna Fryskowska, Piotr Walczykowski and Jolanta Siewert

Remote Sens. 2019, 11(10), 1214; https://doi.org/10.3390/rs11101214 - 22 May 2019

Cited by 35 | Viewed by 5000

Abstract

Unmanned aerial vehicle (UAV) imagery has been widely used in remote sensing and photogrammetry for some time. Increasingly often, apart from recording images in the red-green-blue (RGB) range, multispectral images are also recorded. It is important to accurately assess the radiometric quality of UAV imagery to eliminate interference that might reduce the interpretation potential of the images and distort the results of remote sensing analyses. Such assessment should consider the influence of the atmosphere and the seasonal and weather conditions at the time of acquiring the imagery. The assessment of the radiometric quality of images acquired in different weather conditions is crucial in terms of improving the interpretation potential of the imagery and improving the accuracy of determining the indicators used in remote sensing and in environmental monitoring. Until now, the assessment of radiometric quality of UAV imagery did not consider the influence of meteorological conditions at different times of year. This paper presents an assessment of the influence of weather conditions on the quality of UAV imagery acquired in the visible range. This study presents the methodology for assessing image quality, considering the weather conditions characteristic of autumn in Central and Eastern Europe. The proposed solution facilitates the assessment of the radiometric quality of images acquired in the visible range. Using the objective indicator of quality assessment developed in this study, images were classified into appropriate categories, allowing, at a later stage, to improve the results of vegetation indices. The obtained results confirm that the proposed quality assessment methodology enables the objective assessment of the quality of imagery acquired in different meteorological conditions. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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19 pages, 3635 KiB

Open AccessArticle

Perceptual Quality Assessment of Pan-Sharpened Images

by Oscar A. Agudelo-Medina, Hernan Dario Benitez-Restrepo, Gemine Vivone and Alan Bovik

Remote Sens. 2019, 11(7), 877; https://doi.org/10.3390/rs11070877 - 11 Apr 2019

Cited by 28 | Viewed by 5303

Abstract

Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused products whose subjectivity renders the assessment of pansharpened data a challenging problem. Most previous research on the development of PS algorithms has only superficially addressed the issue of qualitative evaluation, generally by depicting visual representations of the fused images. Hence, it is highly desirable to be able to predict pan-sharpened image quality automatically and accurately, as it would be perceived and reported by human viewers. Such a method is indispensable for the correct evaluation of PS techniques that produce images for visual applications such as Google Earth and Microsoft Bing. Here, we propose a new image quality assessment (IQA) measure that supports the visual qualitative analysis of pansharpened outcomes by using the statistics of natural images, commonly referred to as natural scene statistics (NSS), to extract statistical regularities from PS images. Importantly, NSS are measurably modified by the presence of distortions. We analyze six PS methods in the presence of two common distortions, blur and white noise, on PAN images. Furthermore, we conducted a human study on the subjective quality of pristine and degraded PS images and created a completely blind (opinion-unaware) fused image quality analyzer. In addition, we propose an opinion-aware fused image quality analyzer, whose predictions with respect to human perceptual evaluations of pansharpened images are highly correlated. Full article

(This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users)

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