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Remote Sensing
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Multi-Scale Remote Sensing and Image Analysis
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Multi-Scale Remote Sensing and Image Analysis

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

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 15369

Special Issue Editors

Dr. Peter Hofmann

E-Mail Website
Guest Editor
1. ITC, University Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
2. Deggendorf Institute of Technology, Dieter-Görlitz-Platz 1, 94469 Deggendorf, Germany
Interests: remote sensing; (object-based) image analysis; artificial intelligence; GIScience
Special Issues, Collections and Topics in MDPI journals
Dr. Stefan Lang

E-Mail Website
Guest Editor
Assoc-Prof, Division Head, Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstr. 30, 5020 Salzburg, Austria, Europe
Interests: Earth observation (EO); big Earth data; multi-scale spatial analysis; spatial data management; object-based image analysis (OBIA); knowledge representation; computer vision; multi-dimensional modelling; validation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing plurality of remote sensing sensors and methods, we have a huge pool of them at our disposal. From this pool we can choose those which are appropriate for our tasks. However, performing this choice strongly depends on the objects and phenomena on the Earth’s surface that we want to detect and observe. Besides the spectral characteristics of objects, the scale at which they occur determines the appropriate sensors, data and methods to be used. Remote sensing sensors and platforms are usually designed to operate within certain spectral specifications and with a certain spatial resolution. Consequently, the appropriate choice of remote sensing sensors, data and methods is bound to the intrinsic scale of the objects and phenomena. As the emergence of VHR remote sensing data has demonstrated, higher spatial resolution requires appropriate methods of image analysis. In addition, many objects, phenomena and processes that we can observe with remote sensing are of multi- or interscale character: they are composites of smaller entities or processes emerging to larger phenomena which are usually not just the sum of their parts. These hierarchical interdependences between objects and phenomena depict the necessity for multi-scale remote sensing and image analysis.

In this Special Issue, we first intend to outline the state-of-the-art regarding multiple scales in remote sensing and discuss the role of scale in remote sensing. Then we introduce recent concepts and frameworks for multi-scale remote sensing, including aspects of semantics, ontologies and knowledge representation of scale and multiple scales. Strategies and methods of multi-scale image processing and image segmentation are a further issue. Combining data from multiple sensors with multiple spatial and temporal resolutions is another aspect we want to focus on. Here, aspects of complementarity, and spatial and temporal focus across different scales as well as the validation of small-scale results by means of large scale results are examples of interesting points. Last but not least we intend to highlight applications of multi scale remote sensing and image analysis from different disciplines.

We would like to invite colleagues to submit articles describing their recent research on any of the following topics:

 

  • The role of scale in remote sensing.
  • Concepts for multi-scale remote sensing and image analysis.
  • Strategies of multi-scale image processing.
  • Multi-scale image segmentation.
  • Multi-scale and multi-temporal remote sensing.
  • Multi-scale and multi-sensor remote sensing.
  • Multi-scale remote sensing applications.

 

Dr. Peter Hofmann
Dr. Stefan Lang

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 submissions that pass pre-check are 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 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

  • Multi-scale remote sensing
  • multi-scale image analysis
  • multi-scale knowledge representation
  • scale concepts in remote sensing
  • multi-scale remote sensing applications

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Published Papers (2 papers)

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23 pages, 5624 KiB  
Article
A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images
by Heqian Qiu, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan and Hengcan Shi
Remote Sens. 2019, 11(13), 1594; https://doi.org/10.3390/rs11131594 - 4 Jul 2019
Cited by 73 | Viewed by 7868
Abstract
Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, [...] Read more.
Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively. Full article
(This article belongs to the Special Issue Multi-Scale Remote Sensing and Image Analysis)
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22 pages, 23109 KiB  
Article
Improving the Accuracy of Open Source Digital Elevation Models with Multi-Scale Fusion and a Slope Position-Based Linear Regression Method
by Yu Tian, Shaogang Lei, Zhengfu Bian, Jie Lu, Shubi Zhang and Jie Fang
Remote Sens. 2018, 10(12), 1861; https://doi.org/10.3390/rs10121861 - 22 Nov 2018
Cited by 15 | Viewed by 5559
Abstract
The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall [...] Read more.
The growing need to monitor changes in the surface of the Earth requires a high-quality, accessible Digital Elevation Model (DEM) dataset, whose development has become a challenge in the field of Earth-related research. The purpose of this paper is to improve the overall accuracy of public domain DEMs by data fusion. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods—the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD), and nonlinear adaptive multi-scale decomposition (N-AMD)—are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D—30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the Ice Cloud and Land Elevation Satellite (ICESat) global land surface altimetry data as a reference. The fused DEMs offer significant improvements of 29.6% and 19.3% in RMSE at a mountainous site, and 27.4% and 15.5% over a low-relief region, compared to the SRTM-1 and AW3D30, respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region, respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM. Full article
(This article belongs to the Special Issue Multi-Scale Remote Sensing and Image Analysis)
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