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Scene classification of remote sensing images by optimizing visual vocabulary concerning scene label information

  • role： First author第一作者
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

  • Email：lyan@whu.edu.cn
  • Introduction：闫利(1966—　)，男，教授，研究方向为摄影测量与遥感，地面三维激光扫描。E-mail：lyan@whu.edu.cn
YAN Li,  
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

ZHU Ruixi,  
  • role： Corresponding author通信作者
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

  • Email：yliu@sgg.whu.edu.cn
  • Introduction：刘异(1983—　)，女，副教授，研究方向为遥感图像分类、城市形态分析。E-mail：yliu@sgg.whu.edu.cn
LIU Yi*,  
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

MO Nan

Resümee

The traditional Bag Of Words (BOW) model disregards the scene label information of remote sensing images and ambiguity or redundancy of visual vocabularies. Hence, utilizing BOW to classify categories with similar backgrounds is unsuitable. Therefore, we propose an image scene classification algorithm based on the optimization of visual words with respect to scene label information to handle the said problem.This paper reports on an image scene classification algorithm based on the optimization of visual words with respect to scene label information. The algorithm procedure is as follows: first, images are divided into patches utilizing Spatial Pyramid Matching, and then Scale Invariant Features Transform (SIFT) features are extracted for each local image patch. These features are then clustered with K-means to form a histogram of each patch at different levels utilizing the Boiman strategy. We adopt Image Frequency as the feature selection method on visual words in each category to eliminate visual vocabulary irrelevant to a specific category and obtain a class-specific codebook. Principal Component Analysis (PCA) is then utilized to eliminate redundant visual vocabulary. Finally, we produce a mixture of class-specific histograms in each image patch at different pyramid levels and a traditional histogram with an adaptive weight. A fusion of histograms will be placed in a Support Vector Machine (SVM).We conducted experiments in this study on standard datasets of scene classification. Five experiments were conducted to demonstrate the performance of proposed algorithm. The first experiment shows that our algorithm performs better than methods that do not consider the scene label information with an increased accuracy of approximately 6 percent. The second experiment shows that the proposed method suitably performs in classifying categories with similar backgrounds and classifying error decreases in most categories. The third experiment demonstrates that the accuracy of the proposed method is higher at each pyramid level, and combined pyramids can offer even higher accuracy. The fourth experiment shows that method utilizing an adaptive weighted fusion method is more accurate than methods without. The final experiment demonstrates that the proposed algorithm performs better than other representative methods under the same conditions.This study proposes a method based on the optimization of visual words with respect to scene label information. This algorithm extracts SIFT features at different levels of pyramids combined with the Boiman strategy to generate universal histograms. DF is adopted as the feature selection method to remove visual words irrelevant to a specific category. PCA is then applied to remove redundancy and obtain class-specific codebook and histograms. Finally, a practical adaptive weighted fusion method that combines the traditional histograms of different levels with the class-specific histogram is proposed and placed in an SVM trainer and classifier. The experiment results show that the proposed algorithm suitably performs in classifying categories with similar backgrounds and displays higher stability. However, the proposed algorithm only considers one SIFT descriptor that corresponds to only one visual word. We can perform experiments on one SIFT descriptor that corresponds to several visual words and other feature selection procedures in future research.

Schlüsselwort

scene classification;class-specific histogram;optimization of visual words;principal component analysis;image frequency;adaptive weighted mixture

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