Special Issue "Multi-Modality Data Classification: Algorithms and Applications"

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

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Dr. Junshi Xia
E-Mail Website
Guest Editor
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: high-performance geo-computation; big earth data; data science
Special Issues and Collections in MDPI journals
Dr. Nicola Falco
E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Building 085B, M/S 74R316C CA, USA
Interests: signal and image processing, machine learning for remote sensing, multimodal data integration, hyperspectral data analysis, remote sensing for precision Ag
Special Issues and Collections in MDPI journals
Dr. Lionel Bombrun
E-Mail Website
Guest Editor
Université de Bordeaux, IMS, UMR 5218, Groupe Signal et Image, Bordeaux, France
Interests: signal and image processing, pattern recognition, texture modeling, hyperspectral image classification, SAR image processing, high resolution remote sensing images analysis
Prof. Jon Atli Benediktsson
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Due to the rapid development of sensor technology, multi-modality remotely sensed datasets (e.g., optical, SAR, and LiDAR) that may differ in imaging mechanism, spatial resolution, and coverage can be achieved. Classification is one of the most important techniques to utilize these multi-modality datasets for land cover/land use and dynamic changes in various applications, e.g., precision agriculture, urban planning, and disaster responses.

The utilization of multi-modality datasets has been an active topic in recent years because they can provide complementary information of the same scene, thus boosting the classification performance. The availability of big remote sensing multi-modality data platforms, e.g, ESA’s Copernicus program, Landsat series, and China GaoFen series, is likely to reinforce this trend.  

However, there still remains unsolved problems with multi-modality datasets, such as spectral/spatial variations, gaps in imaging mechanisms, and sensor-specific features of applications, which should be addressed further. This Special Issue, “Multi-Modality Data Classification: Algorithms and Applications”, will collect original manuscripts that address the above-mentioned challenging of multi-modality data classification, not only in the algorithm domain but also in the application domain. We kindly invite you to contribute to the following (but not exhaustive) topics that fit this Special Issue: multi-modality feature extraction, multi-modality data fusion, deep learning and transfer learning using multi-modality datasets, and classification and change detection of multi-modality datasets for any thematic application (related to urban, agricultural, ecological, and disaster ones) from local to global scales.

Dr. Junshi Xia
Dr. Nicola Falco
Dr. Lionel Bombrun
Prof. Jon Atli Benediktsson
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 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 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

  • classification
  • multi-modality data
  • applications
  • data fusion
  • machine learning
  • applications

Published Papers (2 papers)

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Research

Open AccessArticle
An Integrated Land Cover Mapping Method Suitable for Low-Accuracy Areas in Global Land Cover Maps
Remote Sens. 2019, 11(15), 1777; https://doi.org/10.3390/rs11151777 - 29 Jul 2019
Abstract
In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data [...] Read more.
In land cover mapping, an area with complex topography or heterogeneous land covers is usually poorly classified and therefore defined as a low-accuracy area. The low-accuracy areas are important because they restrict the overall accuracy (OA) of global land cover classification (LCC) data generated. In this paper, low-accuracy areas in China (extracted from the MODIS global LCC maps) were taken as examples, identified as the regions having lower accuracy than the average OA of China. An integrated land cover mapping method targeting low-accuracy regions was developed and tested in eight representative low-accuracy regions of China. The method optimized procedures of image choosing and sample selection based on an existent visually-interpreted regional LCC dataset with high accuracies. Five algorithms and 16 groups of classification features were compared to achieve the highest OA. The support vector machine (SVM) achieved the highest mean OA (81.5%) when only spectral bands were classified. Aspect tended to attenuate OA as a classification feature. The optimal classification features for different regions largely depends on the topographic feature of vegetation. The mean OA for eight low-accuracy regions was 84.4% by the proposed method in this study, which exceeded the mean OA of most precedent global land cover datasets. The new method can be applied worldwide to improve land cover mapping of low-accuracy areas in global land cover maps. Full article
(This article belongs to the Special Issue Multi-Modality Data Classification: Algorithms and Applications)
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Open AccessArticle
A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion
Remote Sens. 2019, 11(6), 681; https://doi.org/10.3390/rs11060681 - 21 Mar 2019
Cited by 1
Abstract
In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification. However, the fusion of these two data sources receive less attention than [...] Read more.
In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification. However, the fusion of these two data sources receive less attention than many other, because of their scarce data availability, and relatively challenging fusion task caused by their distinct imaging geometries. Among the existing fusion methods, including manifold learning-based, kernel-based, ensemble-based, and matrix factorization, manifold learning is one of most celebrated techniques for the fusion of heterogeneous data. Therefore, this paper aims to promote the research in hyperspectral and PolSAR data fusion, by providing a comprehensive comparison between existing manifold learning-based fusion algorithms. We conducted experiments on 16 state-of-the-art manifold learning algorithms that embrace two important research questions in manifold learning-based fusion of hyperspectral and PolSAR data: (1) in which domain should the data be aligned—the data domain or the manifold domain; and (2) how to make use of existing labeled data when formulating a graph to represent a manifold—supervised, semi-supervised, or unsupervised. The performance of the algorithms were evaluated via multiple accuracy metrics of land use land cover classification over two data sets. Results show that the algorithms based on manifold alignment generally outperform those based on data alignment (data concatenation). Semi-supervised manifold alignment fusion algorithms performs the best among all. Experiments using multiple classifiers show that they outperform the benchmark data alignment-based algorithms by ca. 3% in terms of the overall classification accuracy. Full article
(This article belongs to the Special Issue Multi-Modality Data Classification: Algorithms and Applications)
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