Land-cover classification of high-resolution remote sensing image based on multi-classifier fusion and the improved Dempster–Shafer evidence theory

To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.&#160;(1) G. Bessardon and E. Gleeson, &#8220;Using the best available physiography to improve weather forecasts for Ireland,&#8221; in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.(2) E. Walsh, et al., &#8220;Using machine learning to produce a very high-resolution land-cover map for Ireland, &#8221; Advances in Science and Research,&#160; (accepted for publication).(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki(4) D. Frantz, et al., &#8220;National-scale mapping of building height using sentinel-1 and sentinel-2 time series,&#8221; Remote Sensing of Environment, vol. 252, 2021.(5) M. Fitrzyk, et al., &#8220;Esa Copernicus sentinel-1 exploitation activities,&#8221; in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.

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A Study on Object-Oriented Classification of Land Use/Land Cover of Remote Sensing Image

2010 2nd International Workshop on Intelligent Systems and Applications ◽

10.1109/iwisa.2010.5473696 ◽

2010 ◽

Author(s):

Cairong Yue ◽

Rong Zhang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Object Oriented ◽

Remote Sensing Image ◽

Land Use Land Cover

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Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

Remote Sensing ◽

10.3390/rs12020311 ◽

2020 ◽

Vol 12 (2) ◽

pp. 311 ◽

Cited By ~ 6

Author(s):

Chun Liu ◽

Doudou Zeng ◽

Hangbin Wu ◽

Yin Wang ◽

Shoujun Jia ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Land Cover Classification ◽

Urban Land ◽

Small Sample ◽

Connectivity Pattern ◽

Feature Maps ◽

Convolutional Network ◽

Urban Land Cover

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.

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