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Uncertainty in Remote Sensing Image Analysis

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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 (20 February 2018) | Viewed by 62131

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Special Issue Editors

Prof. Dr. Alfred Stein

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Guest Editor

Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: spatial data quality; spatial statistics; remote sensing image analysis; error propagation; fuzzy theory; sampling; spatial big data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yong Ge

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Guest Editor

State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
Interests: information extraction; uncertainty assessment; image processing and analysis; spatial statistics; classification
Special Issues, Collections and Topics in MDPI journals

Dr. Inger Fabris Rotelli

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Guest Editor

Department of Statistics, University of Pretoria, Pretoria 0002, South Africa
Interests: image processing; remote sensing; spatial statistics; statistics; machine learning; GIS

Special Issue Information

Dear Colleagues,

Images obtained from satellites are of an increasing resolution. In addition, the frequency of their observations is increasing, and is expected to increase over the near future. Despite these rapid developments, uncertainty is inherent in images. This occurs in all types of images, sensors and platforms, including multi-spectral (hyper-spectral) images, high spatial resolution images and LiDAR images. Uncertainty is, for example, due to mixed pixels, lack of precise ground control points, atmospheric distortion and vague definition of ground objects. It includes both low accuracy, as well as ambiguous definitions.

The aim of this Special Issue is to showcase methods and solutions that deal with uncertainty in remote sensing images. Typically, image analysis methods, statistical methods and uncertainty modelling and its propagation are of interest. We welcome papers that combine a clear and novel methodological component with a good and interesting application. We encourage papers to also include simulations and toy examples.

Prof. Dr. Alfred Stein
Prof. Dr. Ge Yong
Dr. Inger Fabris Rotelli
Guest Editors

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Keywords

Uncertainty modeling
Information extraction
Image processing and uncertainty analysis
Image analysis
Spatial statistics
Uncertainty propagation
Noise removal
Fuzziness

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Related Special Issue

Uncertainty in Remote Sensing Image Analysis (Second Edition) in Remote Sensing (2 articles)

Published Papers (10 papers)

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Editorial

Jump to: Research, Other

3 pages, 171 KiB

Open AccessEditorial

Introduction to the Special Issue “Uncertainty in Remote Sensing Image Analysis”

by Alfred Stein, Yong Ge and Inger Fabris-Rotelli

Remote Sens. 2018, 10(12), 1975; https://doi.org/10.3390/rs10121975 - 7 Dec 2018

Cited by 2 | Viewed by 2922

Abstract

Images obtained from satellites are of an increasing resolution. [...] Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

Research

Jump to: Editorial, Other

30 pages, 4670 KiB

Open AccessArticle

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

by Nitin Bhatia, Valentyn A. Tolpekin, Alfred Stein and Ils Reusen

Remote Sens. 2018, 10(6), 947; https://doi.org/10.3390/rs10060947 - 14 Jun 2018

Cited by 16 | Viewed by 5169

Abstract

A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (

L_{rs, t} (λ)

). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. [...] Read more.

A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (

L_{rs, t} (λ)

). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (

ρ_{t, pre}

) where effects associated with

L_{rs, t} (λ)

are less influential. The method identifies pixels comprising pure materials from

ρ_{t, pre}

. AOD values at the pure pixels are iteratively estimated using

l_{2}

-norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06–0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5–10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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13 pages, 12889 KiB

Open AccessArticle

Comparison between AMSR2 Sea Ice Concentration Products and Pseudo-Ship Observations of the Arctic and Antarctic Sea Ice Edge on Cloud-Free Days

by Xiaoping Pang, Jian Pu, Xi Zhao, Qing Ji, Meng Qu and Zian Cheng

Remote Sens. 2018, 10(2), 317; https://doi.org/10.3390/rs10020317 - 20 Feb 2018

Cited by 29 | Viewed by 5602

Abstract

In recent years, much attention has been paid to the behavior of passive microwave sea ice concentration (SIC) products for marginal ice zones. Based on the definition of ice edges from ship observations, we identified pseudo-ship observations (PSO) and generated PSO ice edges from twelve cloud-free moderate-resolution imaging spectroradiometer (MODIS) images. Two SIC products of the advanced microwave scanning radiometer 2 (AMSR2) were compared at the PSO ice edges: ARTIST (arctic radiation and turbulence interaction study) sea ice (ASI-SIC) and bootstrap (BST-SIC). The mean values of ASI-SIC pixels located at ice edges were 10.5% and 10.3% for the Arctic and the Antarctic, respectively, and are below the commonly applied 15% threshold, whereas the mean values of corresponding BST-SIC pixels were 23.6% and 27.3%, respectively. The mean values of both ASI-SIC and BST-SIC were lower in summer than in winter. The spatial gaps among the 15% ASI-SIC ice edge, the 15% BST-SIC ice edge and the PSO ice edge were mostly within 35 km, whereas the 15% ASI-SIC ice edge matched better with the PSO ice edge. Results also show that the ice edges were located in the thin ice region, with a mean ice thickness of around 5–8 cm. We conclude that the 15% threshold well determines the ice edge from passive microwave SIC in both the Arctic and the Antarctic. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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Graphical abstract

20 pages, 28594 KiB

Open AccessArticle

Analysis of Azimuthal Variations Using Multi-Aperture Polarimetric Entropy with Circular SAR Images

by Feiteng Xue, Yun Lin, Wen Hong, Qiang Yin, Bingchen Zhang, Wenjie Shen and Yue Zhao

Remote Sens. 2018, 10(1), 123; https://doi.org/10.3390/rs10010123 - 19 Jan 2018

Cited by 16 | Viewed by 5284

Abstract

In conventional synthetic aperture radar (SAR), sensors with a fixed look angle are assumed, and the scattering properties are considered as invariant in the azimuth. In some new SAR modes such as wide-angle SAR and circular SAR (CSAR), the azimuthal angle of view is much larger. Anisotropic targets which have different physical shapes from different angles of view are difficult to interpret in the traditional observation model if variations remain unconsidered. Meanwhile, SAR polarimetry is a powerful tool to analyze and interpret targets’ scattering properties. Anisotropic targets can be precisely described with polarimetric signatures from different angles of view. In this paper, polarimetric data is separated into sub-apertures to provide polarimetric properties from different angles of view. A multi-aperture observation model which contains full polarimetric information from all angles of view is then established. Based on the multi-aperture observation model, multi-aperture polarimetric entropy (MAPE) is defined and is suggested as an extension of polarimetric entropy in multi-aperture situations. MAPE describes both targets’ polarimetric properties and variations across sub-apertures. Variations across the azimuth are analyzed and anisotropic and isotropic targets are identified by MAPE. MAPE can be used in many polarimetric wide angle and CSAR applications. Potential applications in target discrimination and classification are discussed. The effectiveness and advantages of MAPE are demonstrated with polarimetric CSAR data acquired from the Institute of Electronics, Chinese Academy of Sciences airborne CSAR system at P-band. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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15 pages, 7434 KiB

Open AccessArticle

Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

by Yuehong Chen, Ya’nan Zhou, Yong Ge, Ru An and Yu Chen

Remote Sens. 2018, 10(1), 77; https://doi.org/10.3390/rs10010077 - 8 Jan 2018

Cited by 42 | Viewed by 7008

Abstract

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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27 pages, 6708 KiB

Open AccessArticle

Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents

by Kamini Yadav and Russell G. Congalton

Remote Sens. 2018, 10(1), 53; https://doi.org/10.3390/rs10010053 - 30 Dec 2017

Cited by 15 | Viewed by 5593

Abstract

Accurate, consistent and timely cropland information over large areas is critical to solve food security issues. To predict and respond to food insecurity, global cropland products are readily available from coarse and medium spatial resolution earth observation data. However, while the use of satellite imagery has great potential to identify cropland areas and their specific types, the full potential of this imagery has yet to be realized due to variability of croplands in different regions. Despite recent calls for statistically robust and transparent accuracy assessment, more attention regarding the accuracy assessment of large area cropland maps is still needed. To conduct a valid assessment of cropland maps, different strategies, issues and constraints need to be addressed depending upon various conditions present in each continent. This study specifically focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to the United States), Africa and Australia. The process of accuracy assessment was performed using a simple random sampling design employed within defined strata (i.e., Agro-Ecological Zones (AEZ’s) for the US and Africa and a buffer zone approach around the cropland areas of Australia. Continent-specific sample analysis was performed to ensure that an appropriate reference data set was used to generate a valid error matrix indicative of the actual cropland proportion. Each accuracy assessment was performed within the homogenous regions (i.e., strata) of different continents using different sources of reference data to produce rigorous and valid accuracy results. The results indicate that continent-specific modified assessments performed for the three selected continents demonstrate that the accuracy assessment can be easily accomplished for a large area such as the US that has extensive availability of reference data while more modifications were needed in the sampling design for other continents that had little to no reference data and other constraints. Each continent provided its own unique challenges and opportunities. Therefore, this paper describes a tale of these three continents providing recommendations to adapt accuracy assessment strategies and methodologies for validating global cropland extent maps. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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18 pages, 6814 KiB

Open AccessArticle

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

by Nicholas M. Enwright, Lei Wang, Sinéad M. Borchert, Richard H. Day, Laura C. Feher and Michael J. Osland

Remote Sens. 2018, 10(1), 5; https://doi.org/10.3390/rs10010005 - 21 Dec 2017

Cited by 25 | Viewed by 8760

Abstract

While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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19 pages, 4303 KiB

Open AccessArticle

Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016

by Lingfei Shi, Feng Ling, Yong Ge, Giles M. Foody, Xiaodong Li, Lihui Wang, Yihang Zhang and Yun Du

Remote Sens. 2017, 9(11), 1148; https://doi.org/10.3390/rs9111148 - 14 Nov 2017

Cited by 29 | Viewed by 7198

Abstract

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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21 pages, 7391 KiB

Open AccessArticle

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

by Dae Kyo Seo, Yong Hyun Kim, Yang Dam Eo, Wan Yong Park and Hyun Chun Park

Remote Sens. 2017, 9(11), 1163; https://doi.org/10.3390/rs9111163 - 13 Nov 2017

Cited by 64 | Viewed by 6181

Abstract

Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth’s surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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13 pages, 20593 KiB

Open AccessTechnical Note

Use of High-Quality and Common Commercial Mirrors for Scanning Close-Range Surfaces Using 3D Laser Scanners: A Laboratory Experiment

by Adrián J. Riquelme, Belén Ferrer and David Mas

Remote Sens. 2017, 9(11), 1152; https://doi.org/10.3390/rs9111152 - 10 Nov 2017

Cited by 11 | Viewed by 6237

Abstract

Three Dimension (3D) laser scanners enable the acquisition of millions of points of a visible object. Terrestrial laser scanners (TLS) are ground-based scanners, and nowadays the available instruments have the ability of rotating their sensor in two axes, capturing almost any point. Since many sensors can only operate in a vertical position, they cannot capture points located beneath themselves. Consequently, these instruments are generally unable to capture data in a vertical descending direction. Moreover, since the device positioning has certain requirements of space and terrain stability, it is possible that specific regions of interest are outside the reach of the laser. A possible solution is to address the laser beam towards the desired direction by means of a mirror. Common mirrors are very cheap; therefore, they are easy to manipulate and to substitute in case they get broken. However, due to their careless fabrication process, it seems reasonable to think that they are unprecise. In contrast, front-end mirrors are more expensive and delicate, and consequently, deflecting angles are more precise. In this research, we designed a laboratory test to analyze the arising noise when standard and high-quality mirrors are used during the TLS scanning process. The results show that the noise introduced when scanning through a standard mirror is higher than that produced when using a high-quality mirror. However, both cases show that this introduced error is lower than the instrumental error. As a result, this study concludes that it is reasonable to use standard mirrors when scanning in similar conditions to this laboratory test. Full article

(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)

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