Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images

• URL: http://arxiv.org/abs/2110.07782v1
• Date: Fri, 15 Oct 2021 00:29:31 GMT
• Title: Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images
• Authors: Shasvat Desai and Debasmita Ghose
• Abstract summary: Semi-supervised techniques generate pseudo-labels from a small set of labeled examples. We propose to use an active learning-based sampling strategy to select a highly representative set of labeled training data. We report a 27% improvement in mIoU with as little as 2% labeled data using active learning sampling strategies.
• Score: 1.0152838128195467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing data is crucial for applications ranging from monitoring forest fires and deforestation to tracking urbanization. Most of these tasks require dense pixel-level annotations for the model to parse visual information from limited labeled data available for these satellite images. Due to the dearth of high-quality labeled training data in this domain, there is a need to focus on semi-supervised techniques. These techniques generate pseudo-labels from a small set of labeled examples which are used to augment the labeled training set. This makes it necessary to have a highly representative and diverse labeled training set. Therefore, we propose to use an active learning-based sampling strategy to select a highly representative set of labeled training data. We demonstrate our proposed method's effectiveness on two existing semantic segmentation datasets containing satellite images: UC Merced Land Use Classification Dataset and DeepGlobe Land Cover Classification Dataset. We report a 27% improvement in mIoU with as little as 2% labeled data using active learning sampling strategies over randomly sampling the small set of labeled training data.

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