Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions

• URL: http://arxiv.org/abs/2212.02130v1
• Date: Mon, 5 Dec 2022 09:41:06 GMT
• Title: Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions
• Authors: Metehan Yal\c{c}{\i}n, Ahmet Alp K{\i}nd{\i}ro\u{g}lu, Furkan Burak Ba\u{g}c{\i}, Ufuk Uyan, Mahiye Uluya\u{g}mur \"Ozt\"urk
• Abstract summary: We present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. Experiments showed that transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation.

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