FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4361982209> ?p ?o ?g. }

• W4361982209 endingPage "3675" @default.
• W4361982209 startingPage "3645" @default.
• W4361982209 abstract "Nowadays, Satellite Image Time Series (SITS) are commonly employed to derive land cover maps (LCM) to support decision makers in a variety of land management applications. In the most general workflow, the production of LCM strongly relies on available ground truth data to train supervised machine learning models. Unfortunately, this data is not always available due to time-consuming and costly field campaigns. In this scenario, the possibility to transfer a model learnt on a particular year ( <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>source domain</i> ) to a successive period of time ( <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>target domain</i> ), over the same study area, can save time and money. Such a kind of model transfer is challenging due to different acquisition conditions affecting each time period thus, resulting in possible distribution shifts between <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>source</i> and <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>target</i> domains. In the general field of machine learning, Unsupervised Domain Adaptation (UDA) approaches are well suited to cope with the learning of models under distribution shifts between <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>source</i> and <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>target</i> domains. While widely explored in the general computer vision field, they are still under investigated for SITS-based land cover mapping, especially for the temporal transfer scenario. With the aim to cope with this scenario in the context of SITS-based land cover mapping, here we propose Spatially Aligned Domain-Adversarial Neural Network, a framework that combines both adversarial learning and self-training to transfer a classification model from a time period (year) to a successive one on a specific study area. Experimental assessment on a study area located in Burkina Faso characterized by challenging operational constraints demonstrates the significance of our proposal. The obtained results have shown that our proposal outperforms all the UDA competing methods by 7 to 12 points of F1-score across three different transfer tasks." @default.
• W4361982209 created "2023-04-05" @default.
• W4361982209 creator A5005595654 @default.
• W4361982209 creator A5009756079 @default.
• W4361982209 creator A5018231294 @default.
• W4361982209 creator A5068424399 @default.
• W4361982209 creator A5079843695 @default.
• W4361982209 date "2023-01-01" @default.
• W4361982209 modified "2023-10-18" @default.
• W4361982209 title "Temporal-Domain Adaptation for Satellite Image Time-Series Land-Cover Mapping With Adversarial Learning and Spatially Aware Self-Training" @default.
• W4361982209 cites W1974289021 @default.
• W4361982209 cites W2025268122 @default.
• W4361982209 cites W2048679005 @default.
• W4361982209 cites W2133556223 @default.
• W4361982209 cites W2157675604 @default.
• W4361982209 cites W2176673053 @default.
• W4361982209 cites W2261059368 @default.
• W4361982209 cites W2307094448 @default.
• W4361982209 cites W2581906016 @default.
• W4361982209 cites W2593768305 @default.
• W4361982209 cites W2620619599 @default.
• W4361982209 cites W2742878349 @default.
• W4361982209 cites W2767524800 @default.
• W4361982209 cites W2789676998 @default.
• W4361982209 cites W2887300625 @default.
• W4361982209 cites W2895094948 @default.
• W4361982209 cites W2903282641 @default.
• W4361982209 cites W2944174022 @default.
• W4361982209 cites W2955034228 @default.
• W4361982209 cites W2963131120 @default.
• W4361982209 cites W2976120863 @default.
• W4361982209 cites W2978864577 @default.
• W4361982209 cites W2979509742 @default.
• W4361982209 cites W2984353870 @default.
• W4361982209 cites W2997645659 @default.
• W4361982209 cites W2998043922 @default.
• W4361982209 cites W2998460251 @default.
• W4361982209 cites W3039883906 @default.
• W4361982209 cites W3040902738 @default.
• W4361982209 cites W3041133507 @default.
• W4361982209 cites W3084280717 @default.
• W4361982209 cites W3097722521 @default.
• W4361982209 cites W3105127913 @default.
• W4361982209 cites W3159278315 @default.
• W4361982209 cites W3164265265 @default.
• W4361982209 cites W3183434307 @default.
• W4361982209 cites W3185151088 @default.
• W4361982209 cites W3186374016 @default.
• W4361982209 cites W3200065466 @default.
• W4361982209 cites W3204417859 @default.
• W4361982209 cites W3207332237 @default.
• W4361982209 cites W3207590480 @default.
• W4361982209 cites W3207979377 @default.
• W4361982209 cites W4205379033 @default.
• W4361982209 cites W4206617559 @default.
• W4361982209 cites W4229457118 @default.
• W4361982209 cites W4288076010 @default.
• W4361982209 cites W4303980688 @default.
• W4361982209 doi "https://doi.org/10.1109/jstars.2023.3263755" @default.
• W4361982209 hasPublicationYear "2023" @default.
• W4361982209 type Work @default.
• W4361982209 citedByCount "2" @default.
• W4361982209 countsByYear W43619822092023 @default.
• W4361982209 crossrefType "journal-article" @default.
• W4361982209 hasAuthorship W4361982209A5005595654 @default.
• W4361982209 hasAuthorship W4361982209A5009756079 @default.
• W4361982209 hasAuthorship W4361982209A5018231294 @default.
• W4361982209 hasAuthorship W4361982209A5068424399 @default.
• W4361982209 hasAuthorship W4361982209A5079843695 @default.
• W4361982209 hasBestOaLocation W43619822091 @default.
• W4361982209 hasConcept C119857082 @default.
• W4361982209 hasConcept C134306372 @default.
• W4361982209 hasConcept C150899416 @default.
• W4361982209 hasConcept C154945302 @default.
• W4361982209 hasConcept C33923547 @default.
• W4361982209 hasConcept C36503486 @default.
• W4361982209 hasConcept C41008148 @default.
• W4361982209 hasConceptScore W4361982209C119857082 @default.
• W4361982209 hasConceptScore W4361982209C134306372 @default.
• W4361982209 hasConceptScore W4361982209C150899416 @default.
• W4361982209 hasConceptScore W4361982209C154945302 @default.
• W4361982209 hasConceptScore W4361982209C33923547 @default.
• W4361982209 hasConceptScore W4361982209C36503486 @default.
• W4361982209 hasConceptScore W4361982209C41008148 @default.
• W4361982209 hasLocation W43619822091 @default.
• W4361982209 hasLocation W43619822092 @default.
• W4361982209 hasLocation W43619822093 @default.
• W4361982209 hasOpenAccess W4361982209 @default.
• W4361982209 hasPrimaryLocation W43619822091 @default.
• W4361982209 hasRelatedWork W2961085424 @default.
• W4361982209 hasRelatedWork W3046775127 @default.
• W4361982209 hasRelatedWork W3107602296 @default.
• W4361982209 hasRelatedWork W3170094116 @default.
• W4361982209 hasRelatedWork W3209574120 @default.
• W4361982209 hasRelatedWork W4210805261 @default.
• W4361982209 hasRelatedWork W4306674287 @default.
• W4361982209 hasRelatedWork W4312192474 @default.
• W4361982209 hasRelatedWork W4386462264 @default.

• next