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Remote Sensing
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Remote Sensing Image Processing in Poland
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Remote Sensing Image Processing in Poland

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 June 2023) | Viewed by 8733

Special Issue Editors

Prof. Dr. Andrzej Stateczny

E-Mail Website
Guest Editor
Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk Technical University, Narutowicza St. 11/12, Gdansk, Poland
Interests: radar navigation; comparative (terrain reference) navigation; multi-sensor data fusion; automotive navigation; radar and sonar target detection and tracking; sonar imaging and understanding; MBES bathymetry; autonomous navigation; artificial intelligence for navigation; deep learning; geoinformatics, underwater navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Marta Wlodarczyk-Sielicka

E-Mail Website
Guest Editor
Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Interests: spatial big data; spatial analysis; artificial neural networks; deep learning; data fusion; processing of bathymetric data; sea bottom modeling; data reduction
Special Issues, Collections and Topics in MDPI journals
Dr. Mariusz Specht

E-Mail Website
Guest Editor
Department of Transport and Logistics, Gdynia Maritime University, Morska 81-87, 81-225 Gdynia, Poland
Interests: global navigation satellite systems; civil engineering; geomatics; navigation; hydrography; mapping; earth observation; geospatial science; geoinformation; spatial analysis; geodesy; applied mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is intended for the submission of review and original articles related to remote sensing image processing in research conducted in Poland. Image processing including as applied to autonomous vehicles is at the centre of many important developments in both civil and defence applications. New technologies such as multi-sensor data fusion, processing of large amounts of image data or deep learning are changing the quality of application areas, improving remote sensing and imaging systems used today. New ideas such as 3D radar, 3D sonar, LiDAR, cameras, magnetometers, gravimeters and others are supporting revolutionary advances in the development of a range of modern image processing applications. The release also aims to advance other areas of image processing, for example medical, applications of modern devices for data acquisition, data visualisation, image classification techniques, image fusion and other fields.

The Special Issue is open to contributions dealing with many aspects of new insights, current challenges, recent advances, and future perspectives in the field of Remote Sensing Image Processing in Poland.

Prof. Dr. Andrzej Stateczny
Dr. Marta Wlodarczyk-Sielicka
Dr. Mariusz Specht
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

  • artificial intelligence for image processing
  • multi-sensory image data fusion
  • deep learning algorithms for image processing
  • big data remote sensing image processing and classification
  • 3D radar and 3D sonar imaging
  • geomagnetic and gravity image
  • SLAM
  • LiDAR and video imaging
  • remote sensing image based on bathymetry
  • data reduction, feature extraction, image understanding and sensor data processing

Published Papers (4 papers)

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Research

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17 pages, 2456 KiB  
Article
Convolutional Neural Network Reference for Track-Before-Detect Applications
by Przemyslaw Mazurek
Remote Sens. 2023, 15(18), 4629; https://doi.org/10.3390/rs15184629 - 20 Sep 2023
Viewed by 1196
Abstract
TBD (Track-Before-Detect) algorithms allow the detection and tracking of objects of which the signal is lost in the background noise. The use of convolutional neural networks (ConvNN) allows to obtain more effective algorithms than the previous, because it is possible to take into [...] Read more.
TBD (Track-Before-Detect) algorithms allow the detection and tracking of objects of which the signal is lost in the background noise. The use of convolutional neural networks (ConvNN) allows to obtain more effective algorithms than the previous, because it is possible to take into account the background as well as the spatial and temporal characteristics of the tracked object signal. The article presents solutions for taking into account the motion with variable trajectory and speed through segmental interpolation and rectification of the trajectory, which allows the effective convolutional implementation of the TBD algorithm. The boundary of object detection was determined depending on the number of pixels of the object in relation to the number of pixels of the image stack and signal strength for the simplest neural network, so it is possible to analyse and compare more complex solutions with the proposed reference. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing in Poland)
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21 pages, 4696 KiB  
Article
Evaluation of Methods for Estimating Lake Surface Water Temperature Using Landsat 8
by Krzysztof Dyba, Sofia Ermida, Mariusz Ptak, Jan Piekarczyk and Mariusz Sojka
Remote Sens. 2022, 14(15), 3839; https://doi.org/10.3390/rs14153839 - 08 Aug 2022
Cited by 5 | Viewed by 3755
Abstract
Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of [...] Read more.
Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R2 = 0.89, while RMSE = 3.68 °C and R2 = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing in Poland)
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23 pages, 2280 KiB  
Article
Analysis of Transformation Methods of Hydroacoustic and Optoelectronic Data Based on the Tombolo Measurement Campaign in Sopot
by Oktawia Lewicka, Mariusz Specht, Andrzej Stateczny, Cezary Specht, Czesław Dyrcz, Paweł Dąbrowski, Bartosz Szostak, Armin Halicki, Marcin Stateczny and Szymon Widźgowski
Remote Sens. 2022, 14(15), 3525; https://doi.org/10.3390/rs14153525 - 22 Jul 2022
Cited by 2 | Viewed by 1426
Abstract
Measurements in the coastal zone are carried out using various methods, including Global Navigation Satellite Systems (GNSS), hydroacoustic and optoelectronic methods. Therefore, it is necessary to develop coordinate transformation models that will enable the conversion of data from the land and marine parts [...] Read more.
Measurements in the coastal zone are carried out using various methods, including Global Navigation Satellite Systems (GNSS), hydroacoustic and optoelectronic methods. Therefore, it is necessary to develop coordinate transformation models that will enable the conversion of data from the land and marine parts to one coordinate system. The article presents selected issues related to the integration of geodetic and hydrographic data. The aim of this publication is to present the various transformation methods and their effects that relate to the data from the tombolo measurement campaign in Sopot conducted in 2018. Data obtained using GNSS Real Time Kinematic (RTK) measurements, Terrestrial Laser Scanning (TLS), the Unmanned Aerial Vehicle (UAV) and the Unmanned Surface Vehicle (USV) were transformed. On the basis of the coordinate transformation methods used, it can be concluded that the adjustment calculus method obtained the best results for the plane coordinates, while the method of P.S. Dąbrowski et al. obtained the best results for the height coordinates. The standard deviation for the difference of the modelled coordinates acquired by the method of P.S. Dąbrowski et al. with respect to the reference coordinates amounted to: 0.022 m (Northing), 0.040 m (Easting) and 0.019 m (height), respectively, while the adjustment calculus method allowed to obtain the following values: 0.009 m (Northing), 0.005 m (Easting) and 0.359 m (height). It can be assumed that a combination of these two seven-parameter transformation methods would provide the best results. In the future, a new seven-parameter transformation method should be developed based on the synthesis of these two existing methods. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing in Poland)
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15 pages, 1154 KiB  
Technical Note
Machine Learning of Usable Area of Gable-Roof Residential Buildings Based on Topographic Data
by Leszek Dawid, Kacper Cybiński and Żanna Stręk
Remote Sens. 2023, 15(3), 863; https://doi.org/10.3390/rs15030863 - 03 Feb 2023
Viewed by 1146
Abstract
In real estate appraisal, especially of residential buildings, one of the primary evaluation parameters is the property’s usable area. When determining the property price, Polish appraisers use data from comparable transactions included in the Real Estate Price Register (REPR), which is highly incomplete, [...] Read more.
In real estate appraisal, especially of residential buildings, one of the primary evaluation parameters is the property’s usable area. When determining the property price, Polish appraisers use data from comparable transactions included in the Real Estate Price Register (REPR), which is highly incomplete, especially regarding properties’ usable areas. This incompleteness renders the identification of comparable transactions challenging and may lead to incorrect prediction of the property price. We address this challenge by applying machine learning methods to estimate the usable area of buildings with gable roofs based only on their topographic data, which is widely available in Poland in the Database of Topographic Objects (BDOT10k) of Light Detection and Ranging (LiDAR) origin. We show that three features are enough to make accurate predictions of the usable area: the covered area, the building’s height, and the number of stories optionally. A neural network trained on buildings from architectural bureaus reached a 4% median percentage error on the same source and 15% on the real buildings from the city of Koszalin, Poland. Therefore, the proposed method can be applied by appraisers to estimate the usable area of buildings with known transaction prices and solve the problem of finding comparable properties for appraisal. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing in Poland)
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