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Special Issue "Advances in Representation Learning for Remote Sensing Analytics (RLRSA)"

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

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Prof. Weifeng Liu

College of Information and Control Engineering, China University of Petroleum (East China), #66 Changjiang West Road, Huangdao District, Qingdao 266580, Shandong. China
Website | E-Mail
Interests: machine learning; manifold learning; representation learning; multiview learning; image classification; remote sensing image analysis
Guest Editor
Dr. Tongliang Liu

School of Information Technologies, Faculty of Engineering and Information Technologies, The University of Sydney, Room 315, Level 3, J12, Cleveland St, Darlington NSW 2008, Australia
Website | E-Mail
Interests: matrix factorization; transfer learning; multi-task learning; deep learning; remote sensing image understanding
Guest Editor
Dr. Yicong Zhou

Faculty of Science and Technology, University of Macau, E11 Avenida da Universidade, Taipa, Macau, China
Website | E-Mail
Interests: chaotic systems; multimedia security; computer vision; patter recognition; machine learning; hyperspectral image classification
Guest Editor
Prof. Shuying Li

The 16th Institute, China Aerospace Science and Technology Corporation, 08 West Hangtian Road, Xi'an 710100, Shaanxi, China
Website | E-Mail
Interests: remote sensing; band selection; image classification; image segmentation; target detection

Special Issue Information

Dear Colleagues,

With the exploding growth of multi-source/multi-temporal/multi-scale remote sensing data, frequently delivered by remote sensors, it is becoming more and more critical to reveal efficient and effective representation learning methodologies for massive remote sensing analytics. Although many promising achievements of representation learning are reported for signal processing applications, it is still a great challenge to develop distinctive representation learning algorithms for remote sensing analytics.

This Special Issue aims to demonstrate the contribution of representation learning algorithms to the research of remote sensing analytics. It is not difficult to enumerate many examples in this area. For instance, unsupervised learning has been widely-applied for remote sensing image segmentation; semi-supervised learning algorithms significantly boost the performance of multispectral image classification; and deep learning methods obtain state-of-the-art results in many remote sensing applications including geo-localization, semantic labeling, target detection in satellite imagery.

The editors expect to collect a set of recent advances in the related topics, to provide a platform for researchers to exchange their innovative ideas on representation learning solutions for remote sensing analytics, and to bring in interesting utilizations of learning algorithms for particular remote sensing applications.

To summarize, this Special Issue welcomes a broad range of submissions that report novel representation learning techniques for remote sensing analytics. We are especially interested in 1) theoretical advances as well as algorithm developments in representation learning for specific remote sensing analytics problems, 2) reports of practical applications and system innovations in remote sensing analytics, and 3) novel data sets as test bed for new developments, preferably with implemented standard benchmarks. The following list contains topics of interest (but is not limited to them):

  • Multiview learning for multi-spectral remote sensing image analysis
  • Feature extraction/learning for remote sensing images
  • Representation learning for hyperspectral image analysis
  • Metric learning for remote sensing image classification
  • Deep learning algorithms for remote sensing
  • Benchmark algorithms and databases for remote sensing
  • Other applications of representation learning for remote sensing

Prof. Weifeng Liu
Dr. Tongliang Liu
Dr. Yicong Zhou
Prof. Shuying Li
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.

Keywords

  • Remote Sensing Image Analysis
  • Representation Learning
  • Feature Extraction
  • Deep Learning
  • Multiview Learning
  • Hypergraph Image Classification

Published Papers (3 papers)

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Research

Open AccessArticle Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery
Remote Sens. 2018, 10(7), 980; https://doi.org/10.3390/rs10070980
Received: 21 May 2018 / Revised: 6 June 2018 / Accepted: 8 June 2018 / Published: 21 June 2018
Cited by 1 | PDF Full-text (4483 KB) | HTML Full-text | XML Full-text
Abstract
Fusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and
[...] Read more.
Fusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and the conflicts between different results. Dempster–Shafer theory (D–S) is an effective method to model uncertainties and combine multiple evidences. Therefore, in this paper, we proposed an urban change detection method for VHR images by fusing multiple change detection methods with D–S evidence theory. Change vector analysis (CVA), iteratively reweighted multivariate alteration detection (IRMAD), and iterative slow feature analysis (ISFA) were utilized to obtain the candidate change maps. The final change detection result is generated by fusing the three evidences with D–S evidence theory and a segmentation object map. The experiment indicates that the proposed method can obtain the best performance in detection rate, false alarm rate, and comprehensive indicators. Full article
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Graphical abstract

Open AccessArticle Region-Wise Deep Feature Representation for Remote Sensing Images
Remote Sens. 2018, 10(6), 871; https://doi.org/10.3390/rs10060871
Received: 8 May 2018 / Revised: 31 May 2018 / Accepted: 1 June 2018 / Published: 5 June 2018
PDF Full-text (2173 KB) | HTML Full-text | XML Full-text
Abstract
Effective feature representations play an important role in remote sensing image analysis tasks. With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and shown powerful ability in image representation. The
[...] Read more.
Effective feature representations play an important role in remote sensing image analysis tasks. With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and shown powerful ability in image representation. The existing deep feature extraction approaches are usually carried out on the whole image directly. However, such deep feature representation strategies may not effectively capture the local geometric invariance of target regions in remote sensing images. In this paper, we propose a novel region-wise deep feature extraction framework for remote sensing images. First, regions that may contain the target information are extracted from one whole image. Then, these regions are fed into a pre-trained convolutional neural network (CNN) model to extract regional deep features. Finally, the regional deep features are encoded by an improved Vector of Locally Aggregated Descriptors (VLAD) algorithm to generate the feature representation for the image. We conducted extensive experiments on remote sensing image classification and retrieval tasks based on the proposed region-wise deep feature extraction framework. The comparison results show that the proposed approach is superior to the existing CNN feature extraction methods. Full article
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Graphical abstract

Open AccessArticle Discriminant Analysis with Graph Learning for Hyperspectral Image Classification
Remote Sens. 2018, 10(6), 836; https://doi.org/10.3390/rs10060836
Received: 26 April 2018 / Revised: 24 May 2018 / Accepted: 24 May 2018 / Published: 27 May 2018
Cited by 1 | PDF Full-text (395 KB) | HTML Full-text | XML Full-text
Abstract
Linear Discriminant Analysis (LDA) is a widely-used technique for dimensionality reduction, and has been applied in many practical applications, such as hyperspectral image classification. Traditional LDA assumes that the data obeys the Gaussian distribution. However, in real-world situations, the high-dimensional data may be
[...] Read more.
Linear Discriminant Analysis (LDA) is a widely-used technique for dimensionality reduction, and has been applied in many practical applications, such as hyperspectral image classification. Traditional LDA assumes that the data obeys the Gaussian distribution. However, in real-world situations, the high-dimensional data may be with various kinds of distributions, which restricts the performance of LDA. To reduce this problem, we propose the Discriminant Analysis with Graph Learning (DAGL) method in this paper. Without any assumption on the data distribution, the proposed method learns the local data relationship adaptively during the optimization. The main contributions of this research are threefold: (1) the local data manifold is captured by learning the data graph adaptively in the subspace; (2) the spatial information within the hyperspectral image is utilized with a regularization term; and (3) an efficient algorithm is designed to optimize the proposed problem with proved convergence. Experimental results on hyperspectral image datasets show that promising performance of the proposed method, and validates its superiority over the state-of-the-art. Full article
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