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A SHALLOW NEURAL NETWORK MODEL FOR URBAN LAND COVER CLASSIFICATION USING VHR SATELLITE IMAGE FEATURES

GEOSPATIAL WEEK 2023, VOL 10-1(2023)

Budapest Univ Technol & Econ | Univ Nyiregyhaza

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Abstract
Recently, image classification techniques using neural networks have received considerable attention in sustainable urban development, since their applications have an extreme effect on building distribution, infrastructural networks, and water resource management. In this research, a back-propagation shallow neural network model is presented for very high resolution satellite image classification in urban environments. Workflow procedures consider selecting and collecting data, preparing required study areas, extracting distinctive features, and applying the classification process. Visual interpretation is performed to identify observed land cover classes and detect distinctive features in the urban environment. Pre-processing techniques are implemented to present the used images in a more suited form for the classification techniques. A shallow neural network model (supported by MathWorks MATLAB environment) is successfully applied and results are evaluated. The proposed model is tested for classifying both WorldView-2 and WorldView-3 multispectral images with different spatial and spectral characteristics to check the model’s applicability to various kinds of satellite imagery and different study areas. Model outcomes are compared to two well-known classification methods; the Nearest Neighbour object-based method and the Maximum Likelihood pixel-based classifier, to validate and check the model stability. The overall accuracy achieved by the proposed model is 86.25% and 83.25%, while the nearest neighbour approach has obtained 84.50% and 82.75%, and the maximum likelihood classifier has accomplished 82.50% and 80.25% for study area 1 and study area 2 respectively. Obtained results indicate that the developed shallow neural network model achieves a promising accuracy for urban land cover classification in comparison with the standard techniques.
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Key words
Shallow neural networks,image classification,VHR satellite images,urban environments,Land use and land cover
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要点】:本文提出了一种基于反向传播的浅层神经网络模型,用于城市土地覆盖分类,该模型在两个研究区域的WorldView-2和WorldView-3多光谱图像分类中取得了86.25%和83.25%的整体准确率,优于传统的最近邻对象方法和最大似然像素分类器。

方法】:研究流程包括数据收集、准备研究区域、提取特征和应用分类过程,使用MathWorks MATLAB环境支持浅层神经网络模型。

实验】:实验通过对WorldView-2和WorldView-3多光谱图像进行分类来验证模型的适用性,两个研究区域分别为84.50%和82.75%的准确率使用最近邻方法,以及82.50%和80.25%的准确率使用最大似然分类器,结果表明该浅层神经网络模型在城市土地覆盖分类中具有潜力。