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首页> 外文期刊>Journal of visual communication & image representation >Automatic building change image quality assessment in high resolution remote sensing based on deep learning
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Automatic building change image quality assessment in high resolution remote sensing based on deep learning

机译：基于深度学习的高分辨率遥感自动建筑物变化图像质量评估

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摘要

The multi-temporal high-resolution remote sensing (HRRS) images are usually acquired at different imaging angles, with serious noise interferences and obvious building shadows, so that detecting the changes of urban buildings is a problem. In order to address this challenge, a deep learning-based algorithm called ABCDHIDL is proposed to automatically detect the building changes from multi-temporal HRRS images. Firstly, an automatic selection method of labeled samples of building changes based on morphology (ASLSBCM) is proposed. Secondly, a deep learning model (DBN-ELM) for building changes detection based on deep belief network (DBN) and extreme learning machine (ELM) is proposed. A convolution operation is employed to extract the spectral, texture and spatial features and generate a combined low-level features vector for each pixel in the multi-temporal HRRS images. The unlabeled samples are introduced to pre-train the DBN, and the parameters of DBN-ELM are globally optimized by jointly using the ELM classifier and the labeled samples are offered by ASLSBCM to further improve the detection accuracy. In order to evaluate the performance of ABCDHIDL, four groups of double-temporal WorldView2 HRRS images in four different experimental regions are selected respectively as the test datasets, and five other representative methods are used and compared with ABCDHIDL in the experiments of buildings change detection. The results show that ABCDHIDL has higher accuracy and automation level than the other five methods despite its relatively higher time consumption. (C) 2019 Published by Elsevier Inc.
机译：多时相高分辨率遥感影像通常是在不同的成像角度获取的，噪声干扰严重，建筑物阴影明显，因此检测城市建筑物的变化是一个难题。为了解决这一挑战，提出了一种称为ABCDHIDL的基于深度学习的算法，该算法可从多时间HRRS图像中自动检测建筑物的变化。首先，提出了一种基于形态学的建筑物变化标记样本自动选择方法（ASLSBCM）。其次，提出了一种基于深度信念网络（DBN）和极限学习机（ELM）的建筑变化检测的深度学习模型（DBN-ELM）。卷积运算用于提取光谱，纹理和空间特征，并为多时间HRRS图像中的每个像素生成组合的低层特征向量。引入未标记的样本对DBN进行预训练，并结合使用ELM分类器对DBN-ELM的参数进行全局优化，并且ASLSBCM提供标记的样本以进一步提高检测精度。为了评估ABCDHIDL的性能，分别选择四个不同实验区域中的四组双时态WorldView2 HRRS图像作为测试数据集，并使用其他五种代表性方法在建筑物变化检测实验中与ABCDHIDL进行比较。结果表明，尽管ABCDHIDL耗时相对较高，但其准确性和自动化水平却高于其他五种方法。 （C）2019由Elsevier Inc.发布

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