齐永锋,杨 乐,火元莲.基于稀疏非负最小二乘编码的高光谱遥感数据分类方法[J].农业机械学报,2016,47(7):332-337.
Qi Yongfeng,Yang Le,Huo Yuanlian.Hyperspectral Remote Sensing Data Classification Method Based on Sparse Non-negative Least squares Coding[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(7):332-337.
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基于稀疏非负最小二乘编码的高光谱遥感数据分类方法   [下载全文]
Hyperspectral Remote Sensing Data Classification Method Based on Sparse Non-negative Least squares Coding   [Download Pdf][in English]
投稿时间:2015-12-09  
DOI:10.6041/j.issn.1000-1298.2016.07.045
中文关键词:  稀疏非负最小二乘  高光谱遥感  数据分类
基金项目:甘肃省自然科学基金项目(145RJZA183)
作者单位
齐永锋 西北师范大学 
杨 乐 西北师范大学 
火元莲 西北师范大学 
中文摘要:为了提高高光谱遥感影像的分类精度,提出了一种基于稀疏非负最小二乘编码的高光谱数据分类方法。采用非负最小二乘方法,将待测样本表示为训练样本的线性组合,并将得到的系数作为待测样本的特征向量,通过最小误差方法对待测样本进行分类。提出的方法在AVIRIS Indian Pines和萨利纳斯山谷高光谱遥感数据集上进行分类实验,并和主成分分析(PCA)、支持向量机(SVM)和基于稀疏表示分类器(SRC)方法进行比较,在2个数据集上本文方法的总体识别精度分别达到85.31%和99.56%,Kappa系数分别为0.8163和0.9867。实验结果表明本文方法的总体识别精度和Kappa系数都优于另外3种方法,是一种较好的高光谱遥感数据分类方法。
Qi Yongfeng  Yang Le  Huo Yuanlian
Northwest Normal University,Northwest Normal University and Northwest Normal University
Key Words:sparse non-negative least-squares  hyperspectral remote sensing  data classification
Abstract:In order to improve the classification accuracy and reduce computation complexity, a hyperspectral remote sensing data classification method based on sparse non negative least squares coding was proposed. By adopting non negative least squares, the test samples were expressed as a linear combination of training samples, and the obtained coefficients were used as its feature vector. As a result of the non negative constraint, the feature vectors were sparse, which can not only improve the efficiency of the proposed algorithm, but also enhance the discrimination performance of algorithm. At last, the minimizing residual was used to classify the test samples. The experimental verifications of the proposed method were carried out on AVIRIS Indian Pines and Salinas Valley hyperspectral remote sensing data, the classification accuracies of the proposed method were 85.31% and 99.56%, and the Kappa coefficients were 0.8163 and 0.9867, respectively. The proposed method was compared with PCA, SVM and SRC in terms of classification accuracy and Kappa coefficients on two databases, experiment results showed that the proposed method was superior to PCA, SVM and SRC. The proposed approach was valuable for hyperspectral data classification with low computational cost and high classification accuracy, it was a better method of hyperspectral remote sensing data classification.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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