1. National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing，Beijing 100029, China
2. Beijing Research Institute of Uranium Geology，Beijing 100029, China
Abstract：Remote sensing images acquired by UAV-mounted hyperspectral sensors have the advantages of rich spectral information and high spatial resolution, which can provide more effective data for extracting impervious surfaces in towns and cities. However, hyperspectral images contain many bands, information redundancy increases the complexity of model training, and the volume of data space grows exponentially with data dimensions. The limited sample size will be sparsely distributed in high-dimensional space, easily leading to model overfitting. In addition, the traditional extraction method has limited feature learning capability, is ineffective in dealing with high-dimensional data, and fails to focus on the specific material information of the impervious surface. To make more effective use of UAV hyperspectral data to obtain information on impervious surfaces in towns and assess the development of town construction, this study selects Donghuayuan Town, Huailai County, Zhangjiakou City, Hebei Province, as the study area and acquires 150 effective bands from airborne hyperspectral remote sensing data. On this basis, the hyperspectral feature bands applicable to extracting impervious surfaces in towns were selected using stepwise discriminant analysis, validated, and comprehensively analyzed using principal component analysis, band standard deviation, and inter-band correlation, and 14 representative bands were finally identified. Subsequently, a remote sensing impervious surface extraction method based on a convolutional neural network was proposed. By improving the AlexNet network architecture, a deep learning network model containing four convolutional layers, one pooling layer, and two fully connected layers was constructed. Finally, two sets of comparison experiments were designed in the study area to compare the information extraction accuracy of impervious surfaces in hyperspectral raw images with selected feature bands and the information extraction accuracy of the proposed network model with common impervious surface extraction methods, respectively. The experimental results show that the selected combination of feature bands can be used as the best combination of bands for impervious surface extraction, which significantly improves the extraction accuracy of various methods. Meanwhile, the network model proposed in this study is the optimal method for impervious surface extraction, and combined with the optimal band combination, the overall accuracy and Kappa coefficient of the final classification reach 99.07% and 0.988 3, respectively, showing excellent performance. The research results in this paper are of great significance for the sustainable development of town construction and ecological and environmental protection and can provide strong support for research in related fields.
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