Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network
| Autor: | Shoujun Jia, Hangbin Wu, Chun Liu, Doudou Zeng, Liang Xin, Yin Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Channel (digital image) Computer science urban land cover classification high-resolution aerial imagery global contextual information multiscale fusion 0211 other engineering and technologies 02 engineering and technology Land cover 01 natural sciences Convolutional neural network Convolution lcsh:Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences Block (data storage) Contextual image classification business.industry Deep learning Pattern recognition Feature (computer vision) General Earth and Planetary Sciences lcsh:Q Artificial intelligence business |
| Zdroj: | Remote Sensing; Volume 12; Issue 2; Pages: 311 Remote Sensing, Vol 12, Iss 2, p 311 (2020) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12020311 |
| Popis: | Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai. |
| Databáze: | OpenAIRE |
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