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首页> 外文期刊>Journal of Applied Remote Sensing >Fusion of deep learning with adaptive bilateral filter for building outline extraction from remote sensing imagery
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Fusion of deep learning with adaptive bilateral filter for building outline extraction from remote sensing imagery

机译：利用自适应双侧滤波器对遥感图像大纲提取的深度学习融合

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摘要

Solving the problem of building extraction from remote sensing images, which have high spatial resolution, is considered to be one of the most challenging issues in the field of photogrammetry science and remote sensing. The purpose of this study is to present an innovative algorithm, named adaptive bilateral filter (ABF) + segment-based neural network, which is based on the fusion of deep convolutional neural networks (DCNNs) and adaptive ABF and has resulted in improvements in the accuracy of building extraction from remote sensing images with high spatial resolution. The building extraction process in this study includes the following steps: applying the ABF to the research data set and optimizing its parameters in order to improve the building outlines, designing, and training the DCNN, SegNet, based on the improved data set and optimizing it using an adaptive moment estimation algorithm and assessing the impact of applying the ABF + SegNet algorithm to automatic building outline extraction. The proposed algorithm in this study is tested on three sets of remote sensing data from the cities of Potsdam, Indianapolis, and Tehran. The results indicate that the ABF + SegNet algorithm is able to extract the buildings from remote sensing color images with suitable accuracy. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：解决具有高空间分辨率的遥感图像的建筑物提取的问题被认为是摄影测量科学和遥感领域中最具挑战性问题之一。本研究的目的是提出一种创新的算法，该算法名为基于自适应双边滤波器（ABF）+段的神经网络，其基于深度卷积神经网络（DCNNS）和Adaptive ABF的融合，并导致了改进高空间分辨率遥感图像提取的准确性。本研究中的建筑物提取过程包括以下步骤：将ABF应用于研究数据集并优化其参数，以改进基于改进的数据集和优化DCNN，设计和培训DCNN的概要，设计和培训DCNN，SEGNET使用Adaptive Songe估计算法并评估将ABF + SEGNET算法应用于自动构建轮廓提取的影响。本研究中的算法在来自波茨坦，印第安纳波利斯和德黑兰城市的三套遥感数据上进行了测试。结果表明，ABF + SEGNET算法能够以合适的精度从遥感彩色图像中提取建筑物。 （c）2018年光学仪表工程师协会（SPIE）

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