一种快速有效的遥感图像场景分类特征

针对在遥感大数据中如何进行快速有效的场景分类，提出一种图像特征的构建方法.基于非监督学习进行快速二值编码，首先对图像局部训练样本利用非监督学习获取相应滤波器组，然后再使用二值化哈希编码方法对场景单元特征图进行量化，最后统计得到场景全局特征.实验结果表明，该特征描述结合了滤波器组和二进制特征描述子的优点，在保证较高分类精度的前提下，能够大幅度提升计算效率，具有较好的鲁棒性.
For this purpose of the rapid and effective scene interpretation for remote sensing big data, a novel method for feature representation is presented. First of all, the corresponding filter banks of local training samples in image are obtained by unsupervised learning. The feature map of scene units is then quantized based on binary hashing coding. Finally, the global feature of scene is obtained from statistical results. The experimental results demonstrate that, combining with both the advantages of filter bank and binary feature descriptor, the proposed feature can greatly enhance the computational efficiency on the assumption of ensuring high accuracy, which has shown its robustness.