Journal of Applied Remote Sensing

Editor-in-Chief: Ni-Bin Chang, University of Central Florida

The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous other commercial and scientific applications. 

Calls for Papers
How to Submit to a Special Section

To submit a manuscript for consideration in a special section, please prepare the manuscript according to the journal guidelines and use the Online Submission SystemLeaving site. A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer‐reviewed in accordance with the journal's established policies and procedures. Authors have the choice to publish with open access.

Advances in Deep Learning for Hyperspectral Image Analysis and Classification (ADLHI)
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 30 September 2018
Guest Editors
Masoumeh Zareapoor

Lead Guest Editor
Shanghai Jiao Tong University
Shanghai, China
and
Shanghai Jiao Tong University
Institute of Pattern Recognition and Image Processing
Shanghai, China
E-mail: mzarea@sjtu.edu.cn

Jinchang Ren

University of Strathclyde
Department of Electronic and Electrical Engineering
Centre for Signal and Image Processing
Strathclyde Hyperspectral Imaging Centre
Glasgow, Scotland, UK
E-mail: jinchang.ren@strath.ac.uk

Huiyu Zhou

University of Leicester
Department of Informatics
Leicester, England, UK
E-mail: hz143@leicester.ac.uk

Wankou Yang

Southeast University
School of Automation
Nanjing, China
E-mail: wkyang@seu.edu.cn

Scope

In recent years, the analysis of hyperspectral images acquired by remote sensors has gained substantial attention and is becoming an increasingly active research discipline. HSI classification plays a key role in many applications; such as urban development, scene interpretation, monitoring of the earth's surface, etc. However, there are several challenges in hyperspectral data classification, including ultrahigh dimensionality of data, a limited number of labeled instances, and large spatial variability of spectral signature. These challenges degrade the ability to differentiate the pairwise distance between points, thus making it difficult to discriminate the most relevant features, resulting in the classification performance giving wrong or inaccurate results.

Deep learning approaches have shown promise to extract complex and discriminative features and competently learn their representations in a wide variety of computer vision tasks, including image classification, speech recognition, etc. Deep learning holds great promise to fulfill the challenging needs of remote sensing image processing. Interest of the remote sensing field toward deep learning models is growing fast, and many applications have been proposed to address the remote sensing problems. The goal of this special section is to develop and gain new ideas and technologies to facilitate the utility of hyperspectral imaging, and also explore its potential in various applications.

Applications of hyperspectral image (HSI) range from traditional remote sensing, such as mining and precision agriculture, into industry-based applications. Food and pharmaceutical quality inspection, medical applications, and even monitoring of the earth's surface are the examples of advanced HSI applications. As industrial demand increases, the need for more effective and appropriate data analysis techniques that can deal with such massive hyperspectral imagery becomes more pressing.

This special section aims to presents state-of-the-art algorithms and applications for HSI-based deep learning. Original papers that review and report on recent progress in this area or address potential solutions to the opening questions are also welcome. In this special section we will cover the following topics:

  • Novel deep learning architectures and algorithms designed for HSI analysis/classification
  • Feature learning from HSI using deep learning (contains feature extraction/selection dimensionality reduction)
  • Target extraction/detection from HSI using deep learning
  • Multisensor fusion with deep learning
  • Deep learning for large-scale remote sensing images
  • Deep learning model for high-resolution and image quality assessment
  • Compressive sensing, sparse representation, tensor decomposition
  • Deep learning for remote sensing image retrieval
  • Survey on emerging HSI processing and evaluation technologies.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

hyperspectral
CubeSats and NanoSats for Remote Sensing
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 30 November 2018.
Guest Editors
Thomas Pagano

Jet Propulsion Laboratory
California Institute of Technology
Pasadena, California, USA
thomas.s.pagano@jpl.nasa.gov

Charles Norton

NASA Headquarters
Science Mission Directorate
Washington, DC, USA
charles.d.norton@nasa.gov

Scope

Advances in electro-optic remote sensing technologies now enable measurements to be made in a fraction of the size once required in earlier systems. Miniaturization of critical instrument technologies including optical systems, electronics, mechanisms, cryocoolers, and sensors as well as increases in the density of semiconductor electronics and detector arrays now enable instruments to be made significantly smaller while achieving the same or better performance. Additionally, spacecraft technologies including navigation, C&DH, communications, power systems, and structures can be made in a fraction of the size, enabling the entire satellite and instrument to be housed in "CubeSats" (where a single "U" is 10x10x10cm), and "NanoSats" where satellites are significantly smaller than traditional but not necessarily in the "U" form factor. These technologies lead to a significant reduction in instrument, spacecraft, and launch costs, building robustness into current remote sensing programs and enabling new measurements to be made through more opportunity and through constellations of satellites to improve revisit time. Numerous challenges remain, including achieving legacy performance in a small package, power and data rate limitations, and mission reliability, but the capabilities have proven themselves for advancing new scientific measurements as well as technology capabilities applicable to remote sensing science.

This special section of JARS will explore all aspects of remote sensing with CubeSats and NanoSats, including instrument systems to support remote sensing of Earth, moon, planets, comets, asteroids, instrument technologies such as telescopes, spectrometers, imagers in the UV, visible, infrared, microwave, radar, and lidar including fields and particles instruments. Advances in spacecraft technologies are also welcome including novel electronic designs and architectures, power management, communication, and navigation technologies, on-board processing methods and spacecraft bus capabilities.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within 6 weeks of manuscript submission. Special sections are opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

Cubesats
Integrating New Sensing Techniques and Nondestructive Evaluation Systems for Smart Production with Industry 4.0 Standards
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 1 August 2019.
Guest Editors
Sheng-Jen (“Tony”) Hsieh

Rockwell Automation Laboratory
College of Engineering
Texas A&M University
College Station, Texas, USA
hsieh@tamu.edu

Leonard J. Bond

Department of Aerospace Engineering
Department of Mechanical Engineering
Iowa State University
Ames, Iowa, USA
bondlj@iastate.edu

Xiaoyan Han

Infrared Imaging and Materials Characterization Laboratory
College of Engineering
Wayne State University
Detroit, Michigan, USA
xiaoyan.han@wayne.edu

Scope

Advances in Industrial Internet of Things (IIoT), cloud computing, robotics, and big data analytics have enabled the transformation of data into knowledge that can be used to monitor, control, and assess processes, systems, and services in real-time with high accuracy. The incorporation of smart sensory devices and wireless network architectures into these systems is known as Industry 4.0. 

Nondestructive evaluation (NDE) concepts-such as sensing and analysis techniques to examine and ensure the reliability and quality of the materials, products, processes, and systems-have been implemented in industry and services. NDE typically requires a long and tedious setup for each inspection. More importantly, to enable NDE to work accurately in some inspection scenarios, breakthroughs such as new sensing and measurements techniques are needed. Industry 4.0 can provide solutions for these challenges, resulting in smart inspection, real-time and permanent monitoring and evaluation, and NDE 2.0. The Journal of Applied Remote Sensing will publish a special section on integrating new sensing techniques and nondestructive evaluation systems for smart production with Industry 4.0 standards. Potential topics include but are not limited to the following:

  • Innovative sensing and measurement techniques for materials, process, product, or system monitoring and evaluation
  • Case studies and/or showcase of novel sensing techniques and Industry 4.0 in smart inspection or production
  • Novel algorithms (including machine learning and deep learning) and devices such as Industrial Internet of Things (IIoT) for sensing, measurement or evaluation in production environments
  • Multispectral sensing systems and algorithms for smart production
  • Integrated sensor networks and cloud computing for real-time data mining in quality and production control
  • Perspectives and future directions on nondestructive evaluation sensing and analysis techniques (NDE 2.0)

To submit a manuscript for consideration, please prepare the paper according to JARS guidelines and submit via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included. Papers will be peer reviewed in accordance with the journal's established policies and procedures.

Smart Production
Advances in Remote Sensing for Forest Structure and Functions
Publication Date
Special section papers are published as soon as the copyedited and typeset proofs are approved by the author.
Submission Deadline
Manuscripts due 30 June 2019.
Guest Editors
Lin (Tony) Cao

Department of Forest Resources Management
Faculty of Forestry
Nanjing Forestry University
Nanjing, China
lincao@njfu.edu.cn

Yunsheng Wang

Centre of Excellence in Laser Scanning Research
Finnish Geospatial Research Institute FGI
Masala, Finland
yunsheng.wang@nls.fi

Hao Tang

University of Maryland College Park
Department of Geographical Sciences
College Park, Maryland, USA
htang@umd.edu

Scope

Forest structure plays a key role in understanding ecosystem processes and functions as well as managing forest resources and carbon storage. In the last decade, significant progress has been witnessed in methodological development for extracting forest structure information, especially in finding links between forest structure and functions from spaceborne, airborne, unmanned aerial vehicle (UAV), and terrestrial mobile and stationary remote sensing.

Remote sensing data such as light detection and ranging (LiDAR) and digital images or the combination of these datasets continue to be extensively used to enhance the capability for providing information on forest structure and function in different applications. On the plot-level, forest structure measurement is experiencing a significant change resulting from the increasingly available point clouds from single-/multispectral LiDAR scanners, or advanced structure from motion (SfM) techniques with high spatial and spectral resolution images, that are mounted on various aerial and terrestrial platforms. On the tree-level, forest structure information can be extracted through delineating and three-dimensional modeling of individual trees from the point clouds to a high level of detail that includes the branches and leaves, informing our understanding of key forest attributes in fine details. This special section of the Journal of Applied Remote Sensing addresses the advancement of these technologies, specifically of forest structure and function for forestry applications. We encourage papers in the application of LiDAR and image-based point clouds from airborne, UAV, and terrestrial devices such as backpack, handheld, and stationary devices. This section includes but is not limited to the following potential topics:  

  • Utilizing conventional, full-waveform or multispectral airborne LiDAR data in extracting stand- and tree-level metrics (e.g., Lorey's mean height, canopy cover, effective LAI, forest yield, and aboveground biomass etc.).   
  • Assessing consistencies of UAV, backpack, hand-held, and conventional terrestrial LiDAR as well as image-based point clouds for estimating stand- and tree-level metrics (e.g., tree height, DBH, stem density, crown dimension, tree volume, biomass components, and growth etc.).  
  • Exploring ultra-high spatial and multispectral resolution UAV data to improve forest structure mapping capacity (e.g., species classification, phenology, and other biophysical attributes etc.).  
  • Integrating with conventional satellite datasets (e.g., Landsat, Sentinel series) for large-scale or long-term forest mapping.

To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal's established policies and procedures.

Forest Structure
Published Special Sections

Advances in Remote Sensing for Air Quality Management  (October-December 2018)
Guest Editors: Barry Gross, Klaus Schäfer, Philippe Keckhut

Advances in Agro-Hydrological Remote Sensing for Water Resources Conservation (October-December 2018)
Guest Editors: Antonino Maltese and Christopher M. U. Neale

Optics in Atmospheric Propagation and Adaptive Systems
(October-December 2018)
Guest Editors: Karin U. Stein, Szymon Gladysz, Christian Eisele, Vladimir P. Lukin

Recent Advances in Earth Observation Technologies for Agrometeorology and Agroclimatology (April-June 2018)
Guest Editors: Shi-bo Fang, George P. Petropoulos, and Davide Cammarano

Improved Intercalibration of Earth Observation Data (January-March 2018)
Guest Editors: Craig Coburn and Aaron Gerace

Feature and Deep Learning in Remote Sensing Applications (October-December 2017)
Guest Editors: John E. Ball, Derek T. Anderson, Chee Seng Chan

Recent Advances in Geophysical Sensing of the Ocean: Remote and In Situ Methods (July-September 2017)
Guest Editors: Weilin Hou and Robert Arnone

Remote Sensing for Investigating the Coupled Biogeophysical and Biogeochemical Process of Harmful Algal Blooms (January-March 2017)
Guest Editors: Alan Weidemann and Ni-Bin Chang

Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis (October-December 2016)
Guest Editors: Xin Huang, Paolo Gamba, and Bormin Huang

Advances in Remote Sensing for Renewable Energy Development: Challenges and Perspectives (2015)
Guest Editors: Yuyu Zhou, Lalit Kumar, and Warren Mabee

Onboard Compression and Processing for Space Data Systems (2015)
Guest Editors: Enrico Magli and Raffaele Vitulli

Management and Analytics of Remotely Sensed Big Data (2015)
Guest Editors: Liangpei Zhang, Qian (Jenny) Du, and Mihai Datcu

Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics (2014 and 2015)
Guest Editors: Liping Di and Zhengwei Yang

High-Performance Computing in Applied Remote Sensing: Part 3 (2014)
Guest Editors: Bormin Huang, Jiaji Wu, and Yang-Lang Chang

Airborne Hyperspectral Remote Sensing of Urban Environments (2014)
Guest Editors: Qian (Jenny) Du and Paolo Gamba

Progress in Snow Remote Sensing (2014)
Guest Editors: Hongjie Xie, Chunlin Huang, and Tiangang Liang

Advances in Infrared Remote Sensing and Instrumentation (2014)
Guest Editors: Marija Strojnik and Gonzalo Paez

Earth Observation for Global Environmental Change (2014)
Guest Editor: Huadong Guo

Advances in Onboard Payload Data Compression (2013)
Guest Editors: Enrico Magli and Raffaele Vitulli

Advances in Remote Sensing Applications for Locust Habitat Monitoring and Management (2013)
Guest Editors: Ramesh Sivanpillai and Alexandre V. Latchininsky

High-Performance Computing in Applied Remote Sensing: Part 2 (2012)
Guest Editors: Bormin Huang and Antonio Plaza

Advances in Remote Sensing for Monitoring Global Environmental Changes (2012)
Guest Editors: Yuyu Zhou, Qihao Weng, Ni-Bin Chang

High-Performance Computing in Applied Remote Sensing: Part 1 (2011)
Guest Editors: Bormin Huang and Antonio Plaza

Satellite Data Compression (2010)
Guest Editor: Bormin Huang

Remote Sensing for Coupled Natural Systems and Built Environments (2010)
Guest Editor: Ni-Bin Chang

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference - Part 2 (2009)
Guest Editors: John J. Qu and Stephen D. Ambrose

Remote Sensing of the Wenchuan Earthquake (2009)
Guest Editor: Huadong Guo

Remote Sensing Applications to Wildland Fire Research in the Eastern United States: Selected Papers from the 2007 EastFIRE Conference (2008)
Guest Editors: John J. Qu and Stephen D. Ambrose

Aquatic Remote Sensing Applications in Environmental Monitoring and Management (2007)
Guest Editors: Vittorio E. Brando and Stuart Phinn

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