摘要
近年来,协同表示分类(Collaborative Representation Classification,CRC)算法成为高光谱遥感影像分类的研究热点,尤其是切空间协同表示分类(Tangent Space Collaborative Representation,TCRC)利用切平面估计测试样本的局部流形,其分类精度得到了显著提高。为进一步提升高光谱遥感影像分类的准确性和可靠性,提出了基于Boosting的高光谱遥感影像切空间协同表示分类算法(Boosting-based Tangent Space Collaborative Representation Classification,Boost TCRC)。Boost TCRC算法采用TCRC算法作为基分类器,通过Boosting原理自适应地调整训练样本的权重,增大错分样本的权重从而使得分类器专注于较难分类的训练样本,然后在基于残差域融合时根据基分类器的分类表现赋予其权重,最终采用最小重构误差的原则对测试样本进行分类。实验采用HyMap(Hyperspectral Mapper)和AVIRIS(Airbone Visible Infrared Imaging Spectrometer)等高光谱遥感影像数据对所提出算法的性能进行了综合评价,结果表明:基于Boosting的集成方式可有效提升TCRC算法的分类效果。针对HyMap数据,Boost TCRC算法总体分类精度和Kappa系数分别为93.73%和0.920 8,两种精度指标分别高于TCRC算法2.82%和0.032 3,同时分别高于AdaBoost ELM算法1.81%和0.022 5。对于AVIRIS数据,Boost TCRC算法总体分类精度和kappa系数为84.11%和0.812 0,两种精度指标分别高于TCRC算法3.97%和0.049 3,同时分别高于AdaBoost ELM算法12.02%和0.143 6。
Recently,Collaborative Representation Classification(CRC)has attracted much attention in hyperspectral image analysis. Due to uses the tangent plane to estimate the local manifold of the test sample. Tangent Collaborative Representation Classification(TCRC)achieve better performance. Furthermore,in order to improve the classification accuracy and reliability of hyperspectral remote sensing images,a novel Boosting-based Tangent Collaborative Representation ensemble method(Boost TCRC)for hyperspectral image classification is proposed. In this algorithm,Boost TCRC algorithm choose TCRC as base classifier and adjust the weight of the training samples adaptively by using the principle of Boosting. Increasing the weight of the misclassified samples so that the classifier concentrates on the training samples that are difficult to classify. Then assigns the weights according to the classification performance of the base classifier based on the residual domain fusion. Finally,the principle of minimum reconstruction error is adopted to classify the test sample. The performance of the proposed algorithm was comprehensively evaluated by hyperspectral remote sensing image data such as HyMap(Hyperspectral Mapper)and AVIRIS(Airbone Visible Infrared Imaging Spectrometer). The Boosting method can effectively improve the classification effect of the TCRC algorithm. For HyMap data,the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 93.73% and 0.920 8 respectively.Two precision values are higher than TCRC algorithm by 2.82% and 0.032 3,and are higher than the AdaBoost ELM algorithm by 1.81% and 0.022 5. For AVIRIS data,the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 84.11% and 0.8120 respectively. Two precision values are higher than TCRC algorithm by 3.97% and 0.049 3,and are higher than AdaBoost ELM algorithm by 12.02% and 0.143 6.
作者
虞瑶
苏红军
姚文静
Yu Yao;Su Hongjun;Yao Wenjing(School of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China)
出处
《遥感技术与应用》
CSCD
北大核心
2020年第3期634-644,共11页
Remote Sensing Technology and Application
基金
国家自然科学基金项目“高光谱遥感影像多特征优化模型与协同表示分类”(41571325)
“高光谱遥感表示模型与分类器动态集成方法”(41871220)资助。
关键词
切空间协同表示
集成学习
BOOSTING
高光谱遥感分类
Tangent collaborative representation
Ensemble learning
Boosting
Hyperspectral image classification
