基于卷积神经网络的空心村高分影像建筑物检测方法

基于卷积神经网络(CNN)提出了一种适用于空心村高分影像的建筑物自动检测方法,该方法利用多尺度显著性检测来获取包含建筑物信息的显著性区域,然后通过滑动窗口获取显著性区域内目标样本块,再将这些样本块输入训练好的CNN并结合SVM来实现分类。为检验方法有效性,选取高分影像进行实验,结果表明,显著性检测能够有效地获取主要目标,减弱其他无关目标的影响,降低数据冗余;卷积神经网络能够自动学习高层次的特征,基于CNN对高分影像进行建筑物检测,分类准确度可以达到97.6%,表明该方法具有较好的鲁棒性和有效性。

Hollow Village Building Detection Method Using High Resolution Remote Sensing Image Based on CNN

Accurately obtaining the building information in the hollow village areas is important for hollow village renovation and research. With the rapid development of remote sensing technology, remote sensing image resolution has been greatly improved and the ground targets can be obtained from high-resolution remote sensing image. But the traditional methods based on low-level hand-engineered features or mid-level features have great limitation in complex environment, especially in hollow village areas. So it needs to use high-level features to express. Convolution neural network (CNN) has become one of the important methods of ground object recognition and detection. Based on CNN, a novel automatic building detection method was proposed. Firstly, a multi-scale saliency computation was employed to extract building areas and a sliding windows approach was applied to generate candidate regions. And then a CNN was applied to classify the regions. In order to verify the validity of this method, the high resolution remote sensing image of typical hollow village was selected to construct the building sample library. Finally, the model for building interpretation was experimentally studied based on the sample library. The results showed that multi-scale saliency can effectively get the main target, weaken the impact of other unrelated targets, and reduce data redundancy. The CNN can automatically learn the high level feature, and the classification accuracy (ACC) of this method can reach 97.6%. So the proposed method can be used to detect building and it had high practical value to hollow village research and renovation.