Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data

Sun, Qingwei; Chao, Jiangang; Lin, Wanhong; Xu, Zhenying; Chen, Wei; He, Ning

doi:10.3390/rs15204937

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…MPCNet [48] proposed a multi-pool contextual segmentation network with high speed. FT-Net [49] tries to use few-shot learning to improve segmentation results. However, these methods do not consider the segmentation performance under adverse imaging conditions and need to improve adaptability to scene variations.…”

Section: Road Scene Semantic Segmentationmentioning

confidence: 99%

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

Wang,

Zhang,

Zhang

et al. 2024

Remote Sensing

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Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain gaps, especially those based on visual domain prompts. To address these issues, this paper proposes a self-prompt strategy based on uncertainty, guiding the model to continuously focus on regions with high uncertainty (i.e., regions with a larger domain gap). Specifically, we still use the Mean-Teacher architecture with the predicted entropy from the teacher network serving as the input to the prompt module. The prompt module processes uncertain maps and guides the student network to focus on regions with higher entropy, enabling continuous adaptation to new scenes. This is a self-prompting strategy that requires no prior knowledge and is tested on widely used benchmarks. In terms of the average performance, our method outperformed the baseline algorithm in TTA and continual TTA settings of Cityscapes-to-ACDC by 3.3% and 3.9%, respectively. Our method also outperformed the baseline algorithm by 4.1% and 3.1% on the more difficult Cityscapes-to-(Foggy and Rainy) Cityscapes setting, which also surpasses six other current TTA methods.

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Section: Road Scene Semantic Segmentationmentioning

confidence: 99%

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

Wang,

Zhang,

Zhang

et al. 2024

Remote Sensing

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“…The semantic segmentation [1][2][3][4][5][6][7] of road scenes is important for autonomous driving [5], particularly during scene data analyses and behavior decision-making [8]. This technology also has good applications in motion control planning [9,10] and multi-sensor fusion processing [11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

Wang,

Zhang,

Zhang

et al. 2024

Remote Sensing

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Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.

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Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images

Santiago Júnior

2024

Applied Sciences

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Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. Nevertheless, there is still a need for clear pieces of evidence on FSL-related issues in the RS context, such as which of the inference approaches is more suitable: inductive or transductive? Moreover, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? This study aims to address these and other relevant questions in the context of FSL for scene classification in RS images. A comprehensive evaluation was conducted considering eight FSL approaches (three inductive and five transductive) and six scene classification databases. Some conclusions of this research are as follows: (1) transductive approaches are better than inductive ones. In particular, the transductive technique Transductive Information Maximisation (TIM) presented the best overall performance, where in 20 cases it got the first place; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase. The most impressive gains occurred particularly considering the AID (6-way) and RESISC-45 (9-way) datasets. Notably, in the AID dataset, a remarkable 58.412% improvement was achieved in 1-shot tasks going from 10 to 200 epochs; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. These findings can guide RS researchers and practitioners in selecting optimal solutions/strategies for developing their applications demanding few labelled samples.

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Learn to Few-Shot Segment Remote Sensing Images from Irrelevant Data

Cited by 3 publications

References 54 publications

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

Exploring Semantic Prompts in the Segment Anything Model for Domain Adaptation

Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images

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