基于自适应字典的小样本高光谱图像分类方法
Hyperspectral Image Classification Method with Small Sample Set Based on Adaptive Dictionary
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摘要: 在有限标记样本下,为了有效协同空谱信息提高高光谱图像的分类性能,提出了一种基于自适应字典的小样本高光谱图像分类方法。首先,对高光谱图像进行熵率超像素分割,分析标记样本的超像素区域和光谱近邻,将鉴别力高的样本扩展至标记样本集;然后在扩展的标记样本集上分析测试样本的空谱信息,对不同的测试样本精简标记样本集,形成自适应字典;最后在自适应字典上,协同空谱信息重构测试样本,在协同表示中同时考虑重构字典中空谱信息的竞争性。实验结果表明,对比传统的基于光谱的方法和固定窗口尺寸下融合空谱特征的高光谱图像分类方法在印地安农林数据集上,当训练样本数目仅为样本集数目2%时,本文方法总体分类精度为91.45%,比其他方法高3.48~39.52个百分点;在训练样本数为1%的帕维亚大学数据集上,该方法的总体分类精度达到95.54%,比其他方法高2.45~21.63个百分点,验证了本文方法的有效性。Abstract: To effectively utilize the spectral and spatial information of limited labeled training samples in hyperspectral image(HSI) classification,a HSI classification approach with small sample set based on adaptive dictionary was proposed.Firstly,discriminating pixels of each labeled sample were extracted from spatial information with entropy rate segmented superpixels and spectral neighborhood,the training set was then extended by adding the discriminating pixels.Furthermore,the spatial-spectral information of each test sample was analyzed,and its adaptive dictionary was constructed by simplifying the extended training sample set.Finally,the spatial-spectral reconstruction was performed on the adaptive dictionary of each test pixel,where the collaboration and competition among dictionary elements were both considered.To evaluate the performance of the proposed approach,it was compared with some traditional methods by using spectral information and the state-of-the-art methods incorporated traditional information of fixed window size,experimental results on Indian Pines dataset with only 2% training set demonstrated that the overall accuracy of the proposed approach was 91.45%,which was 3.48~39.52 percentage points higher than that of other methods,and the results on Pavia University HSI with 1 % training set showed that the overall accuracy of the proposed approach reached 95.54%,which was 2.45~21.63 percentage points higher than that of others,indicating the effectiveness of the proposed approach.