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Remote Sensing Image Processing and Analysis
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Remote Sensing Image Processing and Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (1 November 2019) | Viewed by 20057

Special Issue Editors

Dr. Mariana Belgiu

E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Interests: satellite image time series analysis; machine learning; semantics in remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alfred Stein

E-Mail Website
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. Maggi Kelly

E-Mail Website
Guest Editor
Department of Environmental Sciences, Policy and Management, University of California, Berkeley, CA 94720, USA
Interests: spatial modeling; remote sensing; drones; lidar; historical archives; surveys; participatory mapping; forests; agriculture; wetlands; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Kirsten de Beurs

E-Mail Website
Guest Editor
Department of Geography and Environmental Sustainability, The University of Oklahoma, 100 East Boyd, St. SEC Suite 566, Norman, OK 73019, USA
Interests: remote sensing; phenology; land cover and land use change; urban

Special Issue Information

Dear Colleagues,

The past few years have witnessed the launch of new Earth Observation satellites, including the Sentinel satellites as part of the Copernicus program led by the European Commission (EC), and the micro-satellites launched by Planet, to name but a few. Due to these technological advances, our remote sensing community now has access to dense time-series of remotely-sensed data at different spatial and spectral resolutions. These data are vital for a wide range of applications, including crop mapping and monitoring, land cover/land use mapping, change detection and disaster management. In addition to advances in the technology used for remote sensing data collection, there have also been important scientific and methodological developments in the processing and analysis of these data. The aim of this Special Issue is to present recent image processing and classification methods developed to turn single-date and multi-temporal satellite images into information.

The contributions to this Special Issue are expected to address: (i) the development of innovative methods to integrate data collected by different sensors; (ii) implementation and testing of advanced satellite image time series data analysis methods capable of addressing the challenges related to the lack of training samples and the availability of irregular time sequences; (iii) studies dedicated to the capabilities of machine learning methods to extract highly variable classes from different remote sensing data in the context of insufficient training samples; (iv) assessment and improvement of the transferability of the machine learning methods; and/or (v) image uncertainty analysis.

We invite both theoretical and application-oriented studies to be submitted to this Special Issue. The contributions may cover the following topics (but not limited to):

  • Multi-source data integration
  • Image fusion
  • Feature extraction
  • Satellite image time series analysis
  • Space–time image statistics
  • Scaling in space and time
  • Machine learning methods for image analysis
  • Transferability of the machine learning methods
  • Uncertainty analysis

Dr. Mariana Belgiu
Prof. Dr. Alfred Stein
Prof. Dr. Maggi Kelly
Prof. Dr. Kirsten de Beurs
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. Sensors 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 2600 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

19 pages, 6727 KiB  
Article
Building Extraction from High–Resolution Remote Sensing Images by Adaptive Morphological Attribute Profile under Object Boundary Constraint
by Chao Wang, Yi Shen, Hui Liu, Kaiguang Zhao, Hongyan Xing and Xing Qiu
Sensors 2019, 19(17), 3737; https://doi.org/10.3390/s19173737 - 29 Aug 2019
Cited by 9 | Viewed by 2981
Abstract
A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships [...] Read more.
A novel adaptive morphological attribute profile under object boundary constraint (AMAP–OBC) method is proposed in this study for automatic building extraction from high-resolution remote sensing (HRRS) images. By investigating the associated attributes in morphological attribute profiles (MAPs), the proposed method establishes corresponding relationships between AMAP–OBC and building characteristics in HRRS images. In the preprocessing step, the candidate object set is extracted by a group of rules for screening of non-building objects. Second, based on the proposed adaptive scale parameter extraction and object boundary constraint strategies, AMAP–OBC is conducted to obtain the initial building set. Finally, a further identification strategy with adaptive threshold combination is proposed to obtain the final building extraction results. Through experiments of multiple groups of HRRS images from different sensors, the proposed method shows outstanding performance in terms of automatic building extraction from diverse geographic objects in urban scenes. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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11 pages, 3462 KiB  
Article
Monitoring Dust Storms in Iraq Using Satellite Data
by Reyadh Albarakat and Venkataraman Lakshmi
Sensors 2019, 19(17), 3687; https://doi.org/10.3390/s19173687 - 24 Aug 2019
Cited by 18 | Viewed by 5861
Abstract
Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. The time between [...] Read more.
Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. The time between June 29 to July 8, 2009 is considered one of the worst dust storm periods of all times and many Iraq is suffered medical problems as a result. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS Surface Reflectance Daily L2G Global 1 km and 500 m data were utilized to calculate the Normalized Difference Dust Index (NDDI). The MYD09GA V006 product was used to monitor, map, and assess the development and spread of dust storms over the arid and semi-arid territories of Iraq. We set thresholds for NDDI to distinguish between water and/or ice cloud and ground features and dust storms. In addition; brightness temperature data (TB) from the Aqua /MODIS thermal band 31 were analyzed to distinguish sand on the land surface from atmospheric dust. We used the MODIS level 2 MYD04 deep blue 550 nm Aerosol Option Depth (AOD) data that maintains accuracy even over bright desert surfaces. We found NDDI values lower than 0.05 represent clouds and water bodies, while NDDI greater than 0.18 correspond to dust storm regions. The threshold of TB of 310.5 K was used to distinguish aerosols from the sand on the ground. Approximately 75% of the territory was covered by a dust storm in 5 July 2009 due to strong and dry northwesterly winds. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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24 pages, 6745 KiB  
Article
Vehicle Detection in Aerial Images Using a Fast Oriented Region Search and the Vector of Locally Aggregated Descriptors
by Chongyang Liu, Yalin Ding, Ming Zhu, Jihong Xiu, Mengyang Li and Qihui Li
Sensors 2019, 19(15), 3294; https://doi.org/10.3390/s19153294 - 26 Jul 2019
Cited by 8 | Viewed by 3171
Abstract
Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these [...] Read more.
Vehicle detection in aerial images plays a significant role in civil and military applications and it faces many challenges including the overhead-view perspective, the highly complex background, and the variants of vehicles. This paper presents a robust vehicle detection scheme to overcome these issues. In the detection stage, we propose a novel algorithm to generate oriented proposals that could enclose the vehicle objects properly as rotated rectangles with orientations. To discriminate the object and background in the proposals, we propose a modified vector of locally aggregated descriptors (VLAD) image representation model with a recently proposed image feature, i.e., local steering kernel (LSK) feature. By applying non-maximum suppression (NMS) after classification, we show that each vehicle object is detected with a single-oriented bounding box. Experiments are conducted on aerial images to compare the proposed method with state-of-art methods and evaluate the impact of the components in the model. The results have proven the robustness of the proposed method under various circumstances and the superior performance over other existing vehicle detection approaches. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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21 pages, 9856 KiB  
Article
A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks
by Hongmin Gao, Dan Yao, Mingxia Wang, Chenming Li, Haiyun Liu, Zaijun Hua and Jiawei Wang
Sensors 2019, 19(15), 3269; https://doi.org/10.3390/s19153269 - 25 Jul 2019
Cited by 30 | Viewed by 4367
Abstract
Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the [...] Read more.
Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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17 pages, 3530 KiB  
Article
Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information
by Muqing Li, Luping Xu, Shan Gao, Na Xu and Bo Yan
Sensors 2019, 19(10), 2385; https://doi.org/10.3390/s19102385 - 24 May 2019
Cited by 6 | Viewed by 2593
Abstract
The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not [...] Read more.
The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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