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Special Issue "Remote Sensing Big Data: Theory, Methods and Applications"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2017)

Special Issue Editors

Guest Editor
Prof. Liping Di

Department of Geography and Geoinformation Science, Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030 USA
Website | E-Mail
Interests: agro-geoinformatics, geospatial cyberinfrastructure; standards and interoperability; digital agriculture; big data; geospatial cloud; web services; geospatial knowledge system; agricultural decision systems; global climate change; remote sensing; geographic information system
Guest Editor
Prof. Qian Du

Department of Electrical and Computer Engineering, Mississippi state University, Starkville, MS 39759, USA
Website | E-Mail
Phone: (662) 325-2035
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Guest Editor
Assoc. Prof. Peng Liu

Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Website | E-Mail
Interests: sparse representation; compressive sensing; deep learning; remote sensing image processing
Guest Editor
Prof. Dr. Lizhe Wang

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Website 1 | Website 2 | E-Mail
Phone: +86 10 8217 8070
Interests: Digital Earth; Remote Sensing Image Processing; High Performance Geocomputing

Special Issue Information

Dear Colleagues,

We have entered an era of big data. Our ability to acquire remote sensing data has been improved to an unprecedented level. For a large ground station (e.g., China Remote Sensing Satellite Ground Station (RSGS)), the volume of global data archive could be on the Exabyte level.

When characterizing big data, it is popular to refer to the 3Vs, i.e., remarkable growths in Volume, Velocity and Variety of data. However, this statement is too general, and big data can also be referred to data from different sources, such as medical data, internet data, remote sensing data, etc. Remote sensing data often differ in resolutions, revisit cycle, spectrum, and mode of imaging. Therefore, we can choose different remote sensing systems and datasets for different applications. For remote sensing big data, the 3Vs could be more concretely extended to characteristics of multi-source, multi-scale, high-dimensional, dynamic-state, isomer, and non-linear characteristics.

It is important for us to consider these more concrete and particular characteristics of remote sensing big data when using remote sensing to extract information and understand geo-processes. These characteristics are fundamental assumptions and priors when we analyze remote sensing big data. More characteristics could provide us more information. However, there is no doubt that most of existing techniques and methods are too limited to solve all the problems of remote sensing big data due to its complexity. Since almost all algorithms and models have to consider the intrinsic and extrinsic characteristics of data, most of the fundamental theories, methods and even applications now have to adapt to the great changes from remote sensing big data. It is both the opportunity and challenge for remote sensing communities.

With these issues in mind, it is time to present the current state-of-the-art theoretical, methodological, and applicational research on remote sensing big data. The topics of interest include, but are not limited to:

•    Fundamental theories for remote sensing data processing, such as data representation, data clean, dimension reduction, feature selection, compressive sensing, deep learning, regression, correlation analysis, data organization and structure, etc.;
•    Methods and techniques for collection, distribution, sharing, and visualization of remote sensing big data;
•    Remote sensing big data processing infrastructures and systems, such as cloud computing, high performance computing, Web computing;
•    Fusion and assimilation of remote sensing big data;
•    Inverse problem and low level vision task with remote sensing big data, such as image denoising, image restoration, image recovery, hyperspectral image un-mixing, SAR image reconstruction, supper-resolution, etc.;
•    Middle level vision task with remote sensing big data, such as image segmentation, image change detection, features extraction, image registration, etc.;
•    High level vision task with remote sensing big data, such as target detection or tracking, classification of scenes, image retrieval, image understanding and etc.;
•    Applications of remote sensing big data (i.e., agriculture, environment, land cover, hydrology, forest, carbon cycle, atmosphere, ocean, Earth surface processes)

Prof. Liping Di
Prof. Qian Du
Assoc. Prof. Peng Liu
Prof. Lizhe Wang
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 papers will be 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 monthly 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 1800 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.

Published Papers (9 papers)

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Editorial

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Open AccessEditorial Remote Sensing Big Data: Theory, Methods and Applications
Remote Sens. 2018, 10(5), 711; https://doi.org/10.3390/rs10050711
Received: 2 May 2018 / Revised: 2 May 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
Cited by 1 | PDF Full-text (658 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...] Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)

Research

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Open AccessArticle A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification
Remote Sens. 2017, 9(9), 917; https://doi.org/10.3390/rs9090917
Received: 25 May 2017 / Revised: 16 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
Cited by 6 | PDF Full-text (2444 KB) | HTML Full-text | XML Full-text
Abstract
Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based
[...] Read more.
Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based algorithm which incorporates appearance and spatial information jointly at multiple scales for land-use scene classification to tackle these problems. Specifically, we introduce a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. The resulting multiscale descriptors are used to generate visual words by a general mapping strategy and produce multiscale correlograms of visual words. Then, an adaptive vector quantization of multiscale correlograms, termed multiscale correlatons, are applied to encode the spatial arrangement of visual words at different scales. Experiments with two publicly available land-use scene datasets demonstrate that our MDDC model is discriminative for efficient representation of land-use scene images, and achieves competitive classification results with state-of-the-art methods. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification
Remote Sens. 2017, 9(8), 848; https://doi.org/10.3390/rs9080848
Received: 11 July 2017 / Revised: 2 August 2017 / Accepted: 7 August 2017 / Published: 16 August 2017
Cited by 12 | PDF Full-text (9400 KB) | HTML Full-text | XML Full-text
Abstract
The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple
[...] Read more.
The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling—spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery
Remote Sens. 2017, 9(7), 666; https://doi.org/10.3390/rs9070666
Received: 30 April 2017 / Revised: 12 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
Cited by 12 | PDF Full-text (5985 KB) | HTML Full-text | XML Full-text
Abstract
Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles
[...] Read more.
Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a significant and challenging problem when further analyzing object-related information for civil and engineering applications. However, the computational efficiency and the separate region generation and localization steps are two big obstacles for the performance improvement of the traditional convolutional neural network (CNN)-based object detection methods. Although recent object detection methods based on CNN can extract features automatically, these methods still separate the feature extraction and detection stages, resulting in high time consumption and low efficiency. As a significant influencing factor, the acquisition of a large quantity of manually annotated samples for HSR remote sensing imagery objects requires expert experience, which is expensive and unreliable. Despite the progress made in natural image object detection fields, the complex object distribution makes it difficult to directly deal with the HSR remote sensing imagery object detection task. To solve the above problems, a highly efficient and robust integrated geospatial object detection framework based on faster region-based convolutional neural network (Faster R-CNN) is proposed in this paper. The proposed method realizes the integrated procedure by sharing features between the region proposal generation stage and the object detection stage. In addition, a pre-training mechanism is utilized to improve the efficiency of the multi-class geospatial object detection by transfer learning from the natural imagery domain to the HSR remote sensing imagery domain. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset were conducted to evaluate the proposed method. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud
Remote Sens. 2017, 9(4), 382; https://doi.org/10.3390/rs9040382
Received: 30 January 2017 / Revised: 7 April 2017 / Accepted: 13 April 2017 / Published: 19 April 2017
Cited by 2 | PDF Full-text (5867 KB) | HTML Full-text | XML Full-text
Abstract
To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS
[...] Read more.
To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity
Remote Sens. 2017, 9(4), 323; https://doi.org/10.3390/rs9040323
Received: 24 January 2017 / Revised: 17 March 2017 / Accepted: 24 March 2017 / Published: 29 March 2017
Cited by 12 | PDF Full-text (1588 KB) | HTML Full-text | XML Full-text
Abstract
Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the
[...] Read more.
Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA). Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions
Remote Sens. 2017, 9(3), 284; https://doi.org/10.3390/rs9030284
Received: 31 January 2017 / Revised: 9 March 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
Cited by 7 | PDF Full-text (3092 KB) | HTML Full-text | XML Full-text
Abstract
Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain
[...] Read more.
Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Soft classification (SC) can produce the proportional fractions of land-covers, on which sub-pixel mapping (SPM) can construct fine resolution land-cover maps to reduce the low-spatial-resolution-problem to some extent. Thus, in this paper, sub-pixel land-cover change detection with the use of different resolution images (SLCCD_DR) is addressed based on SC and SPM. Previously, endmember combinations within pixels are ignored in the LR image, which may result in flawed fractional differences. Meanwhile, the information of a known HR land-cover map is insignificantly treated in the SPM models, which leads to a reluctant SLCCD_DR result. In order to overcome these issues, a novel approach based on a back propagation neural network (BPNN) with different resolution images (BPNN_DR) is proposed in this paper. Firstly, endmember variability per pixel is considered during the SC process to ensure the high accuracy of the derived proportional fractional difference image. After that, the BPNN-based SPM model is constructed by a complete supervised framework. It takes full advantage of the prior known HR image, whether it predates or postdates the LR image, to train the BPNN, so that a sub-pixel change detection map is generated effectively. The proposed BPNN_DR is compared with four state-of-the-art methods at different scale factors. The experimental results using both synthetic data and real images demonstrated that it can outperform with a more detailed change detection map being produced. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery
Remote Sens. 2017, 9(3), 280; https://doi.org/10.3390/rs9030280
Received: 19 January 2017 / Revised: 9 March 2017 / Accepted: 10 March 2017 / Published: 16 March 2017
Cited by 8 | PDF Full-text (10178 KB) | HTML Full-text | XML Full-text
Abstract
Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome
[...] Read more.
Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Open AccessArticle Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining
Remote Sens. 2017, 9(2), 116; https://doi.org/10.3390/rs9020116
Received: 23 October 2016 / Revised: 6 January 2017 / Accepted: 24 January 2017 / Published: 29 January 2017
Cited by 2 | PDF Full-text (16082 KB) | HTML Full-text | XML Full-text
Abstract
Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and
[...] Read more.
Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean. Full article
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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