Detecting changes between the existing building basemaps and newly acquired high spatial resolution remotely sensed (HRS) images is a time-consuming task. This is mainly because of the data labeling and poor performance of hand-crafted features. In this paper, for efficient feature extraction, we propose a fully convolutional feature extractor that is reconstructed from the deep convolutional neural network (DCNN) and pre-trained on the Pascal VOC dataset. Our proposed method extract pixel-wise features, and choose salient features based on a random forest (RF) algorithm using the existing basemaps. A data cleaning method through cross-validation and label-uncertainty estimation is also proposed to select potential correct labels and use them for training an RF classifier to extract the building from new HRS images. The pixel-wise initial classification results are refined based on a superpixel-based graph cuts algorithm and compared to the existing building basemaps to obtain the change map. Experiments with two simulated and three real datasets confirm the effectiveness of our proposed method and indicate high accuracy and low false alarm rate.

中文翻译：

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基于预训练的全卷积特征图的过时建筑地图与新VHR图像之间的自动变化检测

检测现有建筑物底图与新获取的高空间分辨率遥感（HRS）图像之间的变化是一项耗时的任务。这主要是由于数据标记和手工制作功能的性能较差。在本文中，为了进行有效的特征提取，我们提出了一种从深度卷积神经网络（DCNN）重建并在Pascal VOC数据集上进行预训练的全卷积特征提取器。我们提出的方法提取逐像素特征，并使用现有底图基于随机森林（RF）算法选择显着特征。还提出了一种通过交叉验证和标签不确定性估计的数据清洗方法，以选择潜在的正确标签，并将其用于训练RF分类器以从新的HRS图像中提取建筑物。基于基于超像素的图割算法对逐个像素的初始分类结果进行细化，并将其与现有的建筑物底图进行比较以获得变化图。用两个模拟数据集和三个真实数据集进行的实验证实了我们提出的方法的有效性，并表明了该方法的高精度和低误报率。