Aerial Imagery Pixel-level Segmentation

• URL: http://arxiv.org/abs/2012.02024v1
• Date: Thu, 3 Dec 2020 16:09:09 GMT
• Title: Aerial Imagery Pixel-level Segmentation
• Authors: Michael R. Heffels and Joaquin Vanschoren
• Abstract summary: We bridge the performance-gap between popular datasets and aerial imagery data. Our work, using the state-of-the-art DeepLabv3+ Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy validation set.
• Score: 0.4079265319364249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aerial imagery can be used for important work on a global scale. Nevertheless, the analysis of this data using neural network architectures lags behind the current state-of-the-art on popular datasets such as PASCAL VOC, CityScapes and Camvid. In this paper we bridge the performance-gap between these popular datasets and aerial imagery data. Little work is done on aerial imagery with state-of-the-art neural network architectures in a multi-class setting. Our experiments concerning data augmentation, normalisation, image size and loss functions give insight into a high performance setup for aerial imagery segmentation datasets. Our work, using the state-of-the-art DeepLabv3+ Xception65 architecture, achieves a mean IOU of 70% on the DroneDeploy validation set. With this result, we clearly outperform the current publicly available state-of-the-art validation set mIOU (65%) performance with 5%. Furthermore, to our knowledge, there is no mIOU benchmark for the test set. Hence, we also propose a new benchmark on the DroneDeploy test set using the best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52.5%.

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