magistrsko delo
Tomaž Peterkovič (Author), Dušan Gleich (Mentor)

Abstract

Magistrsko delo temelji na obdelavi satelitskih slik in uporabi globokih konvolucijskih nevronskih mrež. V vsebini zaključnega dela je opisano raziskovalno delo s področja uporabe polarimetričnega SAR-a. Namen dela je načrtovanje in izdelovanje sistema, ki bi lahko bil sposoben obdelati satelitsko sliko tako, da se iz nje lahko določi vlažnost tal. Za ocenjevanje le-te so bile uporabljene globoke konvolucijske nevronske mreže, ki so se izkazale za zelo uporabne. V postopku izdelave so bili uporabljeni programi za obdelovanje atmosferskih slik s pomočjo polarimetrije, kot so PolSARpro in SNAP. Za nadaljnjo obdelavo slik in načrtovanje globoke konvolucijske nevronske mreže se je uporabljal programski jezik Python v okolju Visual Studio.

Keywords

Daljinsko zaznavanje;nevronske mreže;ocenjevanje vlažnosti tal;polarimetrija;programska oprema PolSARpro;programski jezik Python;magistrske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher: [T. Peterkovič]
UDC: 520.85:681.542.4(043.2)
COBISS: 83194371 Link will open in a new window
Views: 240
Downloads: 49
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Other data

Secondary language: English
Secondary title: Evaluation of soil humidity using radar images and deep learning
Secondary abstract: The master's thesis is based on the processing of satellite images and the use of deep convolutional neural networks. In the content there is described research work in the field of polarimetric SAR. The purpose of the work is to design and manufacture a system, that could be able to process a satellite image so that soil moisture can be determined from it. To evaluate this, we used deep convolutional neural networks, which we believe could prove very useful. In the developing process, we used programs for processing atmospheric images using polarimetry. such as PolSARpro and SNAP. The Python programming language in the Visual Studio environment was used to further process the images and design the deep convolutional neural network.
Secondary keywords: Remote sensing;neural networks;evaluation of soil humidity;polarimetry;PolSARpro;Python;
Type (COBISS): Master's thesis/paper
Thesis comment: Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko, Elektrotehnika
Pages: X, 84 str.
ID: 13312383