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240620s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2024.3414122
|2 doi
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|a DE-627
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|a eng
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| 100 |
1 |
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|a Liu, Qiang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a SemiRS-COC
|b Semi-Supervised Classification for Complex Remote Sensing Scenes With Cross-Object Consistency
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 01.07.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Semi-supervised learning (SSL), which aims to learn with limited labeled data and massive amounts of unlabeled data, offers a promising approach to exploit the massive amounts of satellite Earth observation images. The fundamental concept underlying most state-of-the-art SSL methods involves generating pseudo-labels for unlabeled data based on image-level predictions. However, complex remote sensing (RS) scene images frequently encounter challenges, such as interference from multiple background objects and significant intra-class differences, resulting in unreliable pseudo-labels. In this paper, we propose the SemiRS-COC, a novel semi-supervised classification method for complex RS scenes. Inspired by the idea that neighboring objects in feature space should share consistent semantic labels, SemiRS-COC utilizes the similarity between foreground objects in RS images to generate reliable object-level pseudo-labels, effectively addressing the issues of multiple background objects and significant intra-class differences in complex RS images. Specifically, we first design a Local Self-Learning Object Perception (LSLOP) mechanism, which transforms multiple background objects interference of RS images into usable annotation information, enhancing the model's object perception capability. Furthermore, we present a Cross-Object Consistency Pseudo-Labeling (COCPL) strategy, which generates reliable object-level pseudo-labels by comparing the similarity of foreground objects across different RS images, effectively handling significant intra-class differences. Extensive experiments demonstrate that our proposed method achieves excellent performance compared to state-of-the-art methods on three widely-adopted RS datasets
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|a Journal Article
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| 700 |
1 |
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|a Yue, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kuang, Yang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Weiying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fang, Leyuan
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 19., Seite 3855-3870
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:33
|g year:2024
|g day:19
|g pages:3855-3870
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2024.3414122
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