陈志坤, 江俊君, 姜鑫维, 白露, 蔡之华. 一种基于改进双边滤波的鲁棒高光谱遥感图像特征提取方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(4): 504-510. DOI: 10.13203/j.whugis20180267
引用本文: 陈志坤, 江俊君, 姜鑫维, 白露, 蔡之华. 一种基于改进双边滤波的鲁棒高光谱遥感图像特征提取方法[J]. 武汉大学学报 ( 信息科学版), 2020, 45(4): 504-510. DOI: 10.13203/j.whugis20180267
CHEN Zhikun, JIANG Junjun, JIANG Xinwei, BAI Lu, CAI Zhihua. Robust Hyperspectral Image Feature Extraction Based on Improved Bilateral Filtering[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 504-510. DOI: 10.13203/j.whugis20180267
Citation: CHEN Zhikun, JIANG Junjun, JIANG Xinwei, BAI Lu, CAI Zhihua. Robust Hyperspectral Image Feature Extraction Based on Improved Bilateral Filtering[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 504-510. DOI: 10.13203/j.whugis20180267

一种基于改进双边滤波的鲁棒高光谱遥感图像特征提取方法

Robust Hyperspectral Image Feature Extraction Based on Improved Bilateral Filtering

  • 摘要: 双边滤波(bilateral filtering,BF)是一种简单有效的高光谱遥感图像(hyperspectral image,HSI)特征提取算法。该算法在非结构相似像素空间距离近时会被分配较大权重,从而降低加权限制效果。提出一种分类选优的双边滤波算法(classified optimization bilateral filtering,COBF),从相邻像素集内选择类别结构最相似的像素组成新的相邻像素集,确保新的相邻像素集中用于加权平均的相邻像素尽可能相似,以提高特征提取效果。使用支持向量机(support vector machine,SVM)对COBF提取的HSI特征分类以验证其有效性。结果显示,当训练样本数量只有10个时,Indian Pines、Salinas和PaviaU的分类精度分别高达83.8%、96.0%和90.6%。

     

    Abstract: BF (bilateral filtering) has been widely considered to be a simple and effective way for extracting HSI (hyperspectral image) features. The algorithm limits the influence of non-structural similar pixels on target pixel by weighting spatial proximity and pixels similarity. However, non-structral similar pixels with a close spatial distance will be assigned larger weights, thereby reducing the effect of weighted limit. Therefore, this paper proposed a COBF (classified optimization BF), it selects the pixels with the most similar category structure from neighbor pixel sets to form a new template, ensuring the neighbor pixels in the new template that applied to the weight distribution as similar as possible in order to improve the features extract result. The COBF algorithm has been successfully applied to the feature extraction of several real HSIs. In order to verify the effectiveness of the proposed algorithm, SVM (support vector machine) was used to classify the HSI features that were extracted by COBF. The experimental results show that the overall accuracy is high when the number of training samples is only 10. The overall accuracies of Indian Pines, Salinas and PaviaU are 83.8%, 96.0% and 90.6%, respectively.

     

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