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Multi-scale feature extraction of hyperspectral image with guided filtering

  • role： First author第一作者
  • Affiliation：

    Faculty of Forestry, Southwest Forestry University, Kunming 650224, China

  • Email：wlgbain@126.com
  • Introduction：王雷光(1982—　)，男，副教授，研究方向为高分辨率/高光谱影像分析及林业应用。E-mail：wlgbain@126.com
Leiguang WANG1,  
  • Affiliation：

    Faculty of Forestry, Southwest Forestry University, Kunming 650224, China

Xiaowang CAO1,  
  • Affiliation：

    Faculty of Forestry, Southwest Forestry University, Kunming 650224, China

Yalan ZHENG1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Printing and Packaging, Wuhan University, Wuhan 430079, China

    School of Design, Southwest Forestry University, Kunming 650224, China

  • Email：daiqinling@126.com
  • Introduction：代沁伶(1982—　)，女，博士研究生，研究方向为图像数字滤波处理及应用研究。E-mail：daiqinling@126.com
Qinling DAI23*

résumé

Features extracted from a single scale cannot effectively express differences among land objects and recognize object boundaries.Thus, hyperspectral image classification suffers from low classification accuracy and the “pepper-and-salt” phenomenon. In this context, we propose a set of multi-scale spatial features that is based on guided filtering to improve the performance of image classification in quantitative accuracy and visual interpretation. The structure-transferring property of the guided filtering is investigated to appropriately represent land objects with different sizes. The proposed feature extraction and classification algorithm consists of three steps. First, Principal Component Analysis (PCA) is used to reduce the dimensionality of hyperspectral images and remove noise. Second, the first several principal components that have the most amount of information are guided by the first principal component to obtain filtered features. Multi-scale features are extracted by guided filtering with increasing radii, which can represent structures of different land cover types. Finally, the feature set is fed into classifiers for image classification. We operated experiments on one synthetic and three hyperspectral datasets, namely, Pavia University, Pavia Center, and Salinas. Multiple multi-scale features, including multi-scale features based on guided filtering, multi-scale morphological, and multi-scale texture features, were extracted. Three advanced classifiers, namely, Support Vector Machine(SVM), Random Forest(RF), and K-Nearest Neighbor(KNN), were considered for comparison. The results of the experiments conducted on the synthetic data set showed that the proposed feature extraction method could better smooth the inner pixels of land objects while preserving object boundaries compared with multi-scale morphological features. Compared with the combination of multi-scale morphological features and SVM, the combination of multi-scale guided filtering features and SVM achieved a 6.5% increment in overall accuracy on the Pavia University dataset. The highest classification accuracies for the Pavia Centre and Salinas images were 98.51% and 98.39%, respectively.These resultswere achieved by the proposed multi-scale guided filtering method. A single scale cannot effectively represent the spatial information of land objects. Thus, multi-scale features are extracted using guided filtering. These features can preserve the spatial structures transferred from the guidance image and smooth the details of the input image due to the structure-transferring property of guided filtering. Land objects with various scales can also be effectively described by these multi-scale filtering features by setting different filtering window sizes. The quantitative results and visual inspection showthat multi-scale filtering features can effectively describe the structures of land objects in comparison with morphological and texture features and thus result in high classification accuracy and better visual quality. The proposed feature is suitable for analyzing land scenes with complicated structures, such as urban areas.

mots-clés

hyperspectral image;guided filter;multi-scale feature;classification;spatial information

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