Knowledge evolution learning: A cost-free weakly supervised semantic segmentation framework for high-resolution land cover classification

Cui, Hao; Zhang, Guo; Chen, Yujia; Li, Xue; Hou, Shasha; Li, Haifeng; Ma, Xiaolong; Guan, Na; Tang, Xuemin

doi:10.1016/j.isprsjprs.2023.11.015

Cited by 2 publications

(1 citation statement)

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“…Enhancing the distinguishability between classes and reducing the variance within classes of the learned representations are key to improving the performance of RSI classification [56][57][58]. Ideally, this goal could easily be achieved by supervised contrastive learning methods [59] that bring samples of the same class closer and separate samples of different classes in the representation space.…”

Section: Supervised Contrastive Learning Module With Labeled Datamentioning

confidence: 99%

CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation

Zhang,

Gu,

et al. 2024

Remote Sensing

Self Cite

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The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitrarily large unlabeled datasets are fine-tuned to the target dataset to overcome data distribution shifts. However, the SSL pre-trained models may contain both useful and useless features for the downstream semantic segmentation task, due to the gap between the SSL tasks and the downstream task. To adapt such pre-trained models to semantic segmentation tasks, traditional supervised fine-tuning methods that use only a small number of labeled samples may drop out useful features due to overfitting. The main reason behind this is that supervised fine-tuning aims to map a few training samples from the high-dimensional, sparse image space to the low-dimensional, compact semantic space defined by the downstream labels, resulting in a degradation of the distinguishability. To address the above issues, we propose a class distinguishability-enhanced self-training (CDEST) method to support global-to-local transfer. First, the self-training module in CDEST introduces a semi-supervised learning mechanism to fully utilize the large amount of unlabeled data in the downstream task to increase the size and diversity of the training data, thus alleviating the problem of biased overfitting of the model. Second, the supervised and semi-supervised contrastive learning modules of CDEST can explicitly enhance the class distinguishability of features, helping to preserve the useful features learned from pre-training while adapting to downstream tasks. We evaluate the proposed CDEST method on four RSI semantic segmentation datasets, and our method achieves optimal experimental results on all four datasets compared to supervised fine-tuning as well as three semi-supervised fine-tuning methods.

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Section: Supervised Contrastive Learning Module With Labeled Datamentioning

confidence: 99%