MRELBP特征、Franklin矩和SVM相结合的遥感图像建筑物识别方法

为了进一步提高遥感图像建筑物区域的识别精度,提出了一种基于中值稳健扩展局部二值模式(median robust extended local binary pattern,MRELBP)、Franklin矩和布谷鸟优化支持向量机(support vector machine,SVM)的分类方法。首先,通过MRELBP特征算子计算图像块的纹理特征向量,并根据Franklin矩得到形状特征向量,组合图像块的纹理特征向量和形状特征向量得到综合特征向量;然后,利用训练样本对SVM进行训练,同时由布谷鸟搜索算法对SVM的核函数参数和惩罚因子进行优化;最后,通过训练好的SVM得到建筑物区域识别结果。通过30组试验的结果表明,与基于三原色(red green blue,RGB)和SVM的分类方法、基于LBP和SVM的分类方法、基于Zernike矩和SVM的分类方法相比,本文提出的方法所识别的遥感图像建筑物区域准确度更高。

Building area recognition method of remote sensing image based on MRELBP feature,Franklin moment and SVM

To further improve the recognition accuracy of remote sensing image building area recognition, a classification method based on median robust extended local binary pattern(MRELBP) feature, Franklin moment and optimized support vector machine(SVM) by cuckoo search is proposed. Firstly, calculate the texture feature vector of the image block with MRELBP feature operator and use Franklin moment obtain the shape feature vector, the texture feature vector and the shape feature vector are combined into a comprehensive feature vector. Then train the SVM with training image samples, meanwhile, use cuckoo search to optimize the kernel function parameter as well as the penalty factor. Lastly, input the recognizing image into the SVM to get the result of building area recognition. The results of 30 groups of experiments show that, compared with the classification method based on RGB and SVM, the classification method based on LBP and SVM and the classification method based on Zernike moment and SVM, the accuracy of the remote sensing image building area identified by the proposed method is higher.