稠密特征编码的遥感场景分类算法

针对遥感影像快速有效的场景分类,提出了一种低维度稠密特征编码的场景分类算法.首先提取遥感图像不同尺度下的稠密特征,利用Hellinger kernel对原始特征进行映射变换形成新的特征空间,采用主成分分析对新的特征降维并进行Fisher编码量化,进而实现遥感图像的低维度稠密特征表达,最后在线性支持向量机中完成遥感影像的...

Remote Sensing Image Classification Based on Dense Feature Coding

To classify the scenes in remote sensing images efficiently and effectively,we propose a low-dimensional dense feature coding based scene classification method.Firstly,we extract dense features from remote sensing images at multiple scales and map the original features to a new feature space by using Hellinger kernel.Then,we perform dimensionality reduction and quantization for the obtained features by using PCA and Fisher coding to obtain low-dimensional dense features of the sensing image.Finally,we classify the scenes by using a linear support vector machine.We validate the performance of the proposed algorithm by conducting extensive experiments on three public scene classification datasets.Experimental results demonstrate the following advantages 1) compared with the traditional low or middle-level semantic feature,the classification accuracy has been significantly improved;2) compare with the deep learning-based method,the proposed method can make a good trade-off between computational complexity and classification accuracy,and meet the practical requirements.