Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (1): 20-35.doi: 10.11947/j.AGCS.2024.20220571

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

An feature optimization selection method of SEaTH considering discretization degree

QU Wei, WANG Yuhao, WANG Le, LI Jiuyuan, LI Da   

  1. College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China
  • Received:2022-10-29 Revised:2023-11-05 Published:2024-02-06
  • Supported by:
    The National Natural Science Foundation of China(Nos. 42174006; 42090055); Science Fund for Distinguished Young Scholars of Shaanxi Province(No. 2022JC-18); The Fundamental Research Funds for the Central Universities, CHD(Nos. 300102263201; 300102262902)

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Abstract: Feature selection is one of the key steps in object-oriented information extraction. In view of the fact that the separability and threshold (SEaTH), a feature selection method, does not consider the discrete degree of eigenvalues, only uses the J-M distance to judge a single feature, there may be strong differences between features, and the inability to effectively determine the limitations of the classification order in practical application. Therefore, the extraction of ground objects cannot achieve the optimal effect, the extraction rules of ground objects are also complex, and the portability of the classification model is still poor. To solve these problems, an improved SEaTH algorithm (optimized SEaTH, OPSEaTH) was developed in this study. First, a feature evaluation index (E) is constructed by OPSEaTH based on J-M distance, which can effectively solve the dispersion of eigenvalues. Further, a feature combination evaluation index (Ce) is constructed based on E value, which can effectively evaluate the best feature combination of each feature and automatically determine the classification order of features. Then, the effective classification of feature objects can be completed based on eCognition and other classifiers. In this study, the new algorithm is tested by using GF-2 remote sensing image data, and compared with the classification results of SEaTH algorithm, DPC (density peaks cluster), OIF (optimal index factor), and the nearest neighbor classifier, respectively. The results show that: OPSEaTH algorithm can not only effectively reduce the feature dimension and optimize the feature space, but also automatically and reasonably determine the classification order. The overall accuracy, Kappa coefficient and other accuracy indexes of the OPSEaTH algorithm are significantly better than the feature selection results based on SEaTH algorithm. In addition, the OPSEaTH algorithm is superior to DPC, OIF and the nearest neighbor classifier in terms of feature dimension reduction effect, classification accuracy and computational efficiency. OPSEaTH algorithm is a better feature selection method.

Key words: SEaTH algorithm, feature selection, dispersion coefficient, feature combination, classification order, optimized SEaTH algorithm

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