深度先验图像特征在城市遥感大数据中的应用

Application of Image Feature Extraction Based on Depth Prior in Urban Remote Sensing Big Data

图像特征提取始终是计算机视觉和图像处理的核心任务.随着深度学习的快速发展,卷积神经网络逐渐取代传统图像特征算子,成为特征提取的主要算法.本文针对城市遥感数据众包标记系统中的数据关联问题,结合卷积神经网络和池化编码,提出基于深度先验的图像特征提取方法.该特征能有效聚焦室外图像近处物体,并通过图像检索实验验证了其对室外图像的良好表征能力.

Image feature extraction is always the core task of computer vision and image processing. With the rapid development of deep learning, the Convolutional Neural Network（CNN） has gradually replaced the traditional image feature operator and became the main algorithm for feature extraction. Combined with CNN and sum pooling, we propose a new image feature extraction algorithm based on depth prior aiming at the data association problem in the crowd sourcing labeling system for urban remote sensing data. The feature can effectively focus on the objects in the vicinity of outdoor images and verify their good characterization of outdoor images via image retrieval experiments.