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Enhanced Representation of High-Temporal-Resolution Remote Sensing Data

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 18303

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Special Issue Editors

Prof. Dr. Rajiv Ranjan

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Guest Editor

School of Computing, Newcastle University, Newcastle upon Tyne, UK
Interests: edge computing; cloud computing; big data; Internet of Things

Prof. Dr. Dan Chen

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Guest Editor

School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: neuroengineering; data science; AI

Prof. Dr. Prem Prakash Jayaraman

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Guest Editor

Faculty of Science, Engineering & Technology, Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals

Dr. Deepak Puthal

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Guest Editor

School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: cyber security; blockchain; fog/edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, surveillance video technology plays a more and more important role in smart city applications. For example, an increasing number of low-earth-orbit satellites provide us with the opportunity to make frequent surveillance from space. An increasing number of surveillance cameras inside cities help us better monitor them continuously. These critical applications capture images and videos through constant observing (of a place). Such (remote) sensing data are routinely of a high-temporal resolution. The capability to analyse them has boosted numerous killer applications that were previously impossible, such as monitoring activities of factories on ports, monitoring the urban traffic flow, autopiloting and disaster management.

Taking RS data via satellites as an example, there is historical information accumulated of the currently captured place, possibly of variant resolutions, seasons and illumination. It can be beneficial to enhance the representation of the current data for more accurate analysis and efficient compression, and a priori information can help to make up for the current low quality of sensing data. Moreover, monitoring of a region with satellite videos demands real-time playback of the scene on Earth, but conflicts exist between the high bitrate of the dynamic data and the narrow satellite–Earth transmission bandwidth. The historical prior information can help to reduce the redundancy of the data for compressive representation of the data.

However, to make use of historical data for representation enhancement of the current data, challenges still remain on (I) how to eliminate inferential factors and model the prior knowledge from a large amount of historical data, (II) how to integrate prior knowledge with current data to enhance the representation, and (III) whether the prior knowledge model should be general for all applications or specific to different applications. Hence, in order to extract the benefits of the recently launched low cost satellites with high-frequency data, there is a pressing need to address the challenges in enhanced representation from historical data. Therefore, this Special Issue seeks original research papers that report on new approaches, methods, systems and solutions on the enhanced representation of high-frequency remotely sensed data.

Prof. Dr. Rajiv Ranjan
Prof. Dr. Dan Chen
Dr. Prem Prakash Jayaraman
Dr. Deepak Puthal
Guest Editors

Related References

[1] Xue, Jie & Leung, Yee & Fung, Tung. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes". Remote Sensing，Vol. 11. 324-345, 2019.

[2] X. Wang, R. Hu, Z. Wang and J. Xiao, "Virtual Background Reference Frame Based Satellite Video Coding," in IEEE Signal Processing Letters, vol. 25, no. 10, pp. 1445-1449, 2018.
[3] Hongyang Lu & Jingbo Wei & Lizhe Wang & Peng Liu & Qiegen Liu & Yuhao Wang & Xiaohua Deng. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes". Remote Sensing，Vol. 8. 499-518, 2016.

Prof. Dr. Rajiv Ranjan
Guest Editor

Manuscript Submission Information

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

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

Keywords

Modelling of prior knowledge
Date integration
Reference-based super resolution
Data compression of high-temporal-resolution RS
Historical data aided analysis
Real-time scene understanding

Published Papers (5 papers)

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Research

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21 pages, 3633 KiB

Open AccessArticle

Spatial-Temporal Distribution Analysis of Industrial Heat Sources in the US with Geocoded, Tree-Based, Large-Scale Clustering

by Yan Ma, Caihong Ma, Peng Liu, Jin Yang, Yuzhu Wang, Yueqin Zhu and Xiaoping Du

Remote Sens. 2020, 12(18), 3069; https://doi.org/10.3390/rs12183069 - 19 Sep 2020

Cited by 3 | Viewed by 2307

Abstract

Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO₂ emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach. Full article

(This article belongs to the Special Issue Enhanced Representation of High-Temporal-Resolution Remote Sensing Data)

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19 pages, 3319 KiB

Open AccessArticle

Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics

by Zayneb Ahmed, Abir Jaafar Hussain, Wasiq Khan, Thar Baker, Haya Al-Askar, Janet Lunn, Raghad Al-Shabandar, Dhiya Al-Jumeily and Panos Liatsis

Remote Sens. 2020, 12(6), 1004; https://doi.org/10.3390/rs12061004 - 20 Mar 2020

Cited by 2 | Viewed by 4057

Abstract

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search. Full article

(This article belongs to the Special Issue Enhanced Representation of High-Temporal-Resolution Remote Sensing Data)

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24 pages, 17230 KiB

Open AccessArticle

Geometric Accuracy Improvement Method for High-Resolution Optical Satellite Remote Sensing Imagery Combining Multi-Temporal SAR Imagery and GLAS Data

by Quansheng Zhu, Wanshou Jiang, Ying Zhu and Linze Li

Remote Sens. 2020, 12(3), 568; https://doi.org/10.3390/rs12030568 - 08 Feb 2020

Cited by 7 | Viewed by 4101

Abstract

With the widespread availability of satellite data, a single region can be described using multi-source and multi-temporal remote sensing data, such as high-resolution (HR) optical imagery, synthetic aperture radar (SAR) imagery, and space-borne laser altimetry data. These have become the main source of data for geopositioning. However, due to the limitation of the direct geometric accuracy of HR optical imagery and the effect of the small intersection angle of HR optical imagery in stereo pair orientation, the geometric accuracy of HR optical imagery cannot meet the requirements for geopositioning without ground control points (GCPs), especially in uninhabited areas, such as forests, plateaus, or deserts. Without satellite attitude error, SAR usually provides higher geometric accuracy than optical satellites. Space-borne laser altimetry technology can collect global laser footprints with high altitude accuracy. Therefore, this paper presents a geometric accuracy improvement method for HR optical satellite remote sensing imagery combining multi-temporal SAR Imagery and GLAS data without GCPs. Based on the imaging mechanism, the differences in the weight matrix determination of the HR optical imagery and SAR imagery were analyzed. The laser altimetry data with high altitude accuracy were selected and applied as height control point in combined geopositioning. To validate the combined geopositioning approach, GaoFen2 (GF2) optical imagery, GaoFen6 (GF6) optical imagery, GaoFen3 (GF3) SAR imagery, and the Geoscience Laser Altimeter System (GLAS) footprint were tested. The experimental results show that the proposed model can be effectively applied to combined geopositioning to improve the geometric accuracy of HR optical imagery. Moreover, we found that the distribution and weight matrix determination of SAR images and the distribution of GLAS footprints are the crucial factors influencing geometric accuracy. Combined geopositioning using multi-source remote sensing data can achieve a plane accuracy of 1.587 m and an altitude accuracy of 1.985 m, which is similar to the geometric accuracy of geopositioning of GF2 with GCPs. Full article

(This article belongs to the Special Issue Enhanced Representation of High-Temporal-Resolution Remote Sensing Data)

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17 pages, 10920 KiB

Open AccessArticle

Learned Representation of Satellite Image Series for Data Compression

by Liang Liao, Jing Xiao, Yating Li, Mi Wang and Ruimin Hu

Remote Sens. 2020, 12(3), 497; https://doi.org/10.3390/rs12030497 - 04 Feb 2020

Cited by 6 | Viewed by 3445

Abstract

Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to model the complex evolution of the landscape appearance under different conditions by making use of the historical image series. Under this representation model, the image is disentangled into the content part and the style part. The former represents the consistent landscape structure, while the latter represents the conditional parameters of the environment. To utilize the knowledge learned from the historical image series, we generate synthetic reference frames for the compression of video frames through image translation by the representation model. The synthetic reference frames can highly boost the compression efficiency by changing the original intra-frame prediction to inter-frame prediction for the intra-coded picture (I frame). Experimental results show that the proposed representation learning-based compression method can save an average of 44.22% bits over HEVC, which is significantly higher than that using references generated under the same conditions. Bitrate savings reached 18.07% when applied to satellite video data with arbitrarily collected reference images. Full article

(This article belongs to the Special Issue Enhanced Representation of High-Temporal-Resolution Remote Sensing Data)

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Other

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13 pages, 573 KiB

Open AccessLetter

A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

by Wei Chen, Xin Luo, Zhengfa Liang, Chen Li, Mingfei Wu, Yuanming Gao and Xiaogang Jia

Remote Sens. 2020, 12(3), 588; https://doi.org/10.3390/rs12030588 - 10 Feb 2020

Cited by 3 | Viewed by 3573

Abstract

Depth information has long been an important issue in computer vision. The methods for this can be categorized into (1) depth prediction from a single image and (2) binocular stereo matching. However, these two methods are generally regarded as separate tasks, which are accomplished in different network architectures when using deep learning-based methods. This study argues that these two tasks can be achieved using only one network with the same weights. We modify existing networks for stereo matching to perform the two tasks. We first enable the network capable of accepting both a single image and an image pair by duplicating the left image when the right image is absent. Then, we introduce a training procedure that alternatively selects training samples of depth prediction from a single image and binocular stereo matching. In this manner, the trained network can perform both tasks and single-image depth prediction even benefits from stereo matching to achieve better performance. Experimental results on KITTI raw dataset show that our model achieves state-of-the-art performances for accomplishing depth prediction from a single image and binocular stereo matching in the same architecture. Full article

(This article belongs to the Special Issue Enhanced Representation of High-Temporal-Resolution Remote Sensing Data)

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