高分辨率遥感影像复杂场景的汇总统计方法

Summary statistics method for complex scenes of high-resolution remote sensing image

针对高分辨率遥感影像场景的分类,受人类视觉系统从场景中提取汇总统计信息用于场景感知的启发,提出场景汇总统计特征提取方法。该方法提取场景的平均方向信息和视觉杂乱度,利用Gabor滤波器统计场景的平均方向信息,并基于视觉拥堵进行场景的杂乱度度量,然后将两者组合在一起,形成基于汇总统计特征的复杂场景描述。在21类遥感数据集上的实验表明,当训练样本和测试样本各为50幅时,该方法的分类精度比Gist方法高6.5%,比词包模型(BOW)方法高3.22%,且计算简单,同时与Gist相比,不需要人工干预。

For the classification of high-resolution remote sensing images, inspired by human vision system which extracts summary statistics information for scene perception, a feature extraction method based on summary features was proposed. In the method average orientation information and visual clutter were extracted and combined to form a representation based on summary statistics, in which average orientation information was summarized by using Gabor filters and visual clutter was measured based on visual crowding. The experimental results on the classification of 21 classes of remote sensing image set reveal that the classification accuracy of the proposed method is 6.5% higher than Gist and 3.22% higher than Bag-Of-Words （BOW）, when the number of training images and testing images are both 50. It also has lower calculation burden. While compared with Gist, the proposed method doesn＇t need any human intervention.