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New Trends of GEOBIA in Remote Sensing
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New Trends of GEOBIA in Remote Sensing

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 (30 April 2024) | Viewed by 18714

Special Issue Editors

Prof. Dr. Elif Sertel

E-Mail Website
Guest Editor
Department of Geomatics Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey
Interests: remote sensing; deep learning; disaster management; geospatial data analysis; land cover/land use change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jagannath Aryal

E-Mail Website
Guest Editor
Faculty of Engineering and Information technology (FEIT), The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: geomatics, remote sensing; Geo-AI; disaster risk management; sustainability and resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic Object-Based Image Analysis (GEOBIA) has been widely used for accurate map generation from High Resolution (HR) and Very High Resolution (VHR) satellite images, aerial photographs and Unmanned Aerial Vehicle (UAV) images. One of the advantages of GEOBIA is the integration of multi-source, multi-temporal and multi-modal vector and raster geospatial data into the segmentation or classification steps and implementation of various image processing and topological features and functions. With the increasing availability of OpenSource Geoinformation,  developing new strategies for accurate thematic mapping using GEOBIA is an important challenge. The topics of interest include, but are not limited to:

  • Thematic mapping with GEOBIA (Mapping of LULC, Urban areas, Crop types, Forest stand types, Greenhouses, Archeological Sites, etc.),
  • Multi-modal and multi-task implementations for GEOBIA,
  • Ensemble Learning for GEOBIA,
  • Geographic object detection (Buildings, Roads, Airplanes, Ships, etc.) using GEOBIA techniques,
  • Big Geospatial Data, Geospatial Artificial Intelligence (GeoAI) and GEOBIA integration,
  • GEOBIA for information extraction from Atmospheric Monitoring Satellites and Sensors (Sentinel-5P, Suomi-NPP VIIRS, TROPOMI, etc.),
  • Generating high-quality labels for Deep Learning applications using GEOBIA,
  • GEOBIA applications for historical LULC mapping,
  • Integration of Crowd-source data into GEOBIA,
  • New segmentation techniques and image-objects,
  • Multi-disciplinary GEOBIA applications.

Prof. Dr. Elif Sertel
Prof. Dr. Jagannath Aryal
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

  • geomatics
  • GIS
  • earth observation
  • spatial statistics
  • GEOBIA
  • remote sensing
  • geospatial data analysis
  • land cover/land use change

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Published Papers (3 papers)

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Research

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23 pages, 2869 KiB  
Article
Unraveling Segmentation Quality of Remotely Sensed Images on Plastic-Covered Greenhouses: A Rigorous Experimental Analysis from Supervised Evaluation Metrics
by Gizem Senel, Manuel A. Aguilar, Fernando J. Aguilar, Abderrahim Nemmaoui and Cigdem Goksel
Remote Sens. 2023, 15(2), 494; https://doi.org/10.3390/rs15020494 - 13 Jan 2023
Cited by 3 | Viewed by 1657
Abstract
Plastic-covered greenhouse (PCG) segmentation represents a significant challenge for object-based PCG mapping studies due to the spectral characteristics of these singular structures. Therefore, the assessment of PCG segmentation quality by employing a multiresolution segmentation algorithm (MRS) was addressed in this study. The structure [...] Read more.
Plastic-covered greenhouse (PCG) segmentation represents a significant challenge for object-based PCG mapping studies due to the spectral characteristics of these singular structures. Therefore, the assessment of PCG segmentation quality by employing a multiresolution segmentation algorithm (MRS) was addressed in this study. The structure of this work is composed of two differentiated phases. The first phase aimed at testing the performance of eight widely applied supervised segmentation metrics in order to find out which was the best metric for evaluating image segmentation quality over PCG land cover. The second phase focused on examining the effect of several factors (reflectance storage scale, image spatial resolution, shape parameter of MRS, study area, and image acquisition season) and their interactions on PCG segmentation quality through a full factorial analysis of variance (ANOVA) design. The analysis considered two different study areas (Almeria (Spain) and Antalya (Turkey)), seasons (winter and summer), image spatial resolution (high resolution and medium resolution), and reflectance storage scale (Percent and 16Bit formats). Regarding the results of the first phase, the Modified Euclidean Distance 2 (MED2) was found to be the best metric to evaluate PCG segmentation quality. The results coming from the second phase revealed that the most critical factor that affects MRS accuracy was the interaction between reflectance storage scale and shape parameter. Our results suggest that the Percent reflectance storage scale, with digital values ranging from 0 to 100, performed significantly better than the 16Bit reflectance storage scale (0 to 10,000), both in the visual interpretation of PCG segmentation quality and in the quantitative assessment of segmentation accuracy. Full article
(This article belongs to the Special Issue New Trends of GEOBIA in Remote Sensing)
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22 pages, 43106 KiB  
Article
Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction
by Jagannath Aryal and Bipul Neupane
Remote Sens. 2023, 15(2), 488; https://doi.org/10.3390/rs15020488 - 13 Jan 2023
Cited by 8 | Viewed by 3615
Abstract
Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps [...] Read more.
Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-scale feature maps in U-Net and ResUNET with partial concepts of the feature pyramid network (FPN). Furthermore, supervised domain adaptation is investigated to minimise the effects of domain-shift between the two datasets. The datasets include the benchmark WHU Building dataset and a developed dataset with 5× fewer samples, 4× lower spatial resolution and complex high-rise buildings and skyscrapers. The newly developed networks are compared to six state-of-the-art FCNs using five metrics: pixel accuracy, adjusted accuracy, F1 score, intersection over union (IoU), and the Matthews Correlation Coefficient (MCC). The proposed networks outperform the FCNs in the majority of the accuracy measures in both datasets. Compared to the larger dataset, the network trained on the smaller one shows significantly higher robustness in terms of adjusted accuracy (by 18%), F1 score (by 31%), IoU (by 27%), and MCC (by 29%) during the cross-domain validation of MSA-UNET. MSA-ResUNET shows similar improvements, concluding that the proposed networks when trained using domain adaptation increase the robustness and minimise the domain-shift between the datasets of different complexity. Full article
(This article belongs to the Special Issue New Trends of GEOBIA in Remote Sensing)
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Review

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28 pages, 1981 KiB  
Review
Remote Sensing Object Detection in the Deep Learning Era—A Review
by Shengxi Gui, Shuang Song, Rongjun Qin and Yang Tang
Remote Sens. 2024, 16(2), 327; https://doi.org/10.3390/rs16020327 - 12 Jan 2024
Cited by 25 | Viewed by 12294
Abstract
Given the large volume of remote sensing images collected daily, automatic object detection and segmentation have been a consistent need in Earth observation (EO). However, objects of interest vary in shape, size, appearance, and reflecting properties. This is not only reflected by the [...] Read more.
Given the large volume of remote sensing images collected daily, automatic object detection and segmentation have been a consistent need in Earth observation (EO). However, objects of interest vary in shape, size, appearance, and reflecting properties. This is not only reflected by the fact that these objects exhibit differences due to their geographical diversity but also by the fact that these objects appear differently in images collected from different sensors (optical and radar) and platforms (satellite, aerial, and unmanned aerial vehicles (UAV)). Although there exists a plethora of object detection methods in the area of remote sensing, given the very fast development of prevalent deep learning methods, there is still a lack of recent updates for object detection methods. In this paper, we aim to provide an update that informs researchers about the recent development of object detection methods and their close sibling in the deep learning era, instance segmentation. The integration of these methods will cover approaches to data at different scales and modalities, such as optical, synthetic aperture radar (SAR) images, and digital surface models (DSM). Specific emphasis will be placed on approaches addressing data and label limitations in this deep learning era. Further, we survey examples of remote sensing applications that benefited from automatic object detection and discuss future trends of the automatic object detection in EO. Full article
(This article belongs to the Special Issue New Trends of GEOBIA in Remote Sensing)
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