Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling

• URL: http://arxiv.org/abs/2201.01953v2
• Date: Sun, 9 Jan 2022 05:23:03 GMT
• Title: Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling
• Authors: Yang Long and Gui-Song Xia and Liangpei Zhang and Gong Cheng and Deren Li
• Abstract summary: Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content by assigning a semantic label to every pixel of the image. We present a large-scale scene classification dataset that contains one million aerial images termed Million-AID. We also report benchmarking experiments using classical convolutional neural networks (CNNs) to achieve pixel-wise semantic labeling.
• Score: 48.30060717413166
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image. With the popularization of data-driven methods, the past decades have witnessed promising progress on ASP by approaching the problem with the schemes of tile-level scene classification or segmentation-based image analysis, when using high-resolution aerial images. However, the former scheme often produces results with tile-wise boundaries, while the latter one needs to handle the complex modeling process from pixels to semantics, which often requires large-scale and well-annotated image samples with pixel-wise semantic labels. In this paper, we address these issues in ASP, with perspectives from tile-level scene classification to pixel-wise semantic labeling. Specifically, we first revisit aerial image interpretation by a literature review. We then present a large-scale scene classification dataset that contains one million aerial images termed Million-AID. With the presented dataset, we also report benchmarking experiments using classical convolutional neural networks (CNNs). Finally, we perform ASP by unifying the tile-level scene classification and object-based image analysis to achieve pixel-wise semantic labeling. Intensive experiments show that Million-AID is a challenging yet useful dataset, which can serve as a benchmark for evaluating newly developed algorithms. When transferring knowledge from Million-AID, fine-tuning CNN models pretrained on Million-AID perform consistently better than those pretrained ImageNet for aerial scene classification. Moreover, our designed hierarchical multi-task learning method achieves the state-of-the-art pixel-wise classification on the challenging GID, bridging the tile-level scene classification toward pixel-wise semantic labeling for aerial image interpretation.

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