Mapping of Polar Areas Based on High-Resolution Satellite Images: The Example of the Henryk Arctowski Polish Antarctic Station

Kurczyński, Z.; Różycki, Sebastian; Bylina, Paweł

doi:10.1515/rgg-2017-0016

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…By employing multiple radar images and D-InSAR techniques, it becomes possible to monitor subtle centimeterlevel changes, offering tremendous potential for studying Antarctic glacier movement, mass balance and global environmental changes. In a similar vein, the authors of [3] demonstrate the production of polar remote-sensing products using very high-resolution satellite (VHRS) imagery, which proves to be an effective alternative to costlier aerial photographs or ground surveys. Moreover, Ref.…”

Section: Geographic Information Productsmentioning

confidence: 93%

“…Sea ice extraction (SIE) has been a crucial problem in many application aspects, such as the polar navigation [1], terrain analysis [2], polar cartography [3] and polar expedition [4]. With the rapid development of the machine learning technique, computational capability and data acquisition, the SIE problem has reached the deep learning era.…”

Section: Introductionmentioning

confidence: 99%

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Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

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Section: Geographic Information Productsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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“…The article presents descriptions of seven selected masks of the Laplace filter used in the processing and interpretation of digital data of various origin. This may be navigation data (Borawski, 2004;Burger and Burge, 2009a;Stateczny et al, 2020Stateczny et al, , 2021; signal processing (Mallat, 2009;Waheed et al, 2020); radar, sonar and satellite (Jasinski et al, 1999;Stateczny and Nowakowski, 2006;Kurczynski et al, 2017;Fryskowska et al, 2019;Reda and Kedzierski, 2020;Stateczny et al, 2020Stateczny et al, , 2021; geodetic and cartographic, called remote sensing in Kupidura and Kupidura (2009) and Kupidura et al (2010); meteorological in Jasinski et al (1999) -information about clouds including its shape, size, internal structure, as well as the relationship of the location of different types of clouds. These may also be digital images of building infrastructure elements (Burger and Burge, 2009b;Reda and Kedzierski, 2020;Wojtkowska et al, 2021) and technical devices, including military ones (Pokonieczny and Moscicka, 2018).…”

Section: Introductionmentioning

confidence: 99%

Wstęp

2023

Advances in Geodesy and Geoinformation

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The Laplace operator is a differential operator which is used to detect edges of objects in digital images. This paper presents the properties of the most commonly used third-order 3×3 pixels Laplace contour filters including the difference schemes used to derive them. The authors focused on the mathematical properties of the Laplace filters. The basic reasons of the differences of the properties were studied and indicated using their transfer functions and modified differential equations. The relations between the transfer function for the differential Laplace operator and its difference operators were described and presented graphically. The impact of the corner elements of the masks on the results was discussed. This is a theoretical work. The basic research conducted here refers to a few practical examples which are illustrations of the derived conclusions. We are aware that unambiguous and even categorical final statements as well as indication of areas of the results application always require numerous experiments and frequent dissemination of the results. Therefore, we present only a concise procedure of determination of the mathematical properties of the Laplace contour filters matrices. In the next paper we shall present the spectral characteristic of the fifth order filters of the Laplace type.

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“…The paper presents the descriptions of seven selected masks of the Laplace type used in the processing and interpretation of digital data of various origin. As we have already mentioned in Winnicki et al (2022) this may be navigation data (Borawski, 2004;Stateczny and Nowakowski, 2006;Burger and Burge, 2009a;Stateczny et al, 2021) radar, sonar and satellite (Kurczynski et al, 2017;Fryskowska et al, 2019;Stateczny et al, 2020), geodetic and cartographic (called remotely sensed in Kupidura and Kupidura (2009), Kupidura et al (2010)), meteorological (Jasinski et al (1999) -information about clouds including its shape, size, internal structure, as well as the relationship of the location of different types of clouds). These may also be digital images of building infrastructure elements (Burger and Burge, 2009a;Reda and Kedzierski, 2020;Wojtkowska et al, 2021) and technical devices (Pokonieczny and Moscicka, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advances in Geodesy and Geoinformation

2022

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The Laplace operator is a differential operator which is used to detect edges of objects in digital images. This paper presents the properties of the most commonly used fifth-order pixels Laplace filters including the difference schemes used to derive them (finite difference method -FDM and finite element method -FEM). The results of the research concerning third-order pixels matrices of the convolution Laplace filters used for digital processing of images were presented in our previous paper: The mathematical characteristic of the Laplace contour filters used in digital image processing. The third order filters is presented by Winnicki et al. (2022). As previously, the authors focused on the mathematical properties of the Laplace filters: their transfer functions and modified differential equations (MDE). The relations between the transfer function for the differential Laplace operator and its difference operators are described and presented here in graphical form. The impact of the corner elements of the masks on the results is also discussed. A transfer function, is a function characterizing properties of the difference schemes applied to approximate differential operators. Since they are relations derived in both types of spaces (continuous and discrete), comparing them facilitates the assessment of the applied approximation method.

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Mapping of Polar Areas Based on High-Resolution Satellite Images: The Example of the Henryk Arctowski Polish Antarctic Station

Abstract: Abstract

Cited by 4 publications

References 18 publications

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Wstęp

Advances in Geodesy and Geoinformation

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