Supervised feature selection based on interclass distinctions for scene classification

Hainan Wang; Yiming Tang; Xiang Huang; Mei Wu; Yue Meng

doi:10.1117/12.3025675

8 April 2024 Supervised feature selection based on interclass distinctions for scene classification

Hainan Wang, Yiming Tang, Xiang Huang, Mei Wu, Yue Meng

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130903F (2024) https://doi.org/10.1117/12.3025675
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The high intraclass variations and high interclass similarities make high spatial resolution (HSR) image interpretation very difficult. In this paper, we provide a feature selection method for scene classification, particularly to recognize the images from the industrial scene. The proposed method is based on scene overlapping rate and a quantitative feature assessment metric, considering the interclass distinguishing capabilities of features. Thus, the optimal feature representation for diverse scenes according to the discriminative characteristics of intraclass images can be selected. Specifically, an efficient scene recognition algorithm is presented to identify the easy-confused scenes from the dataset. An evaluation metric is also proposed metric to quantify the distinguishing capability of features with full statistical support. Experiments comparing 14 state-of-art classification methods on two challenging benchmark scene datasets (UCM and AID) show the effectiveness of the proposed method for HSR scene classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hainan Wang, Yiming Tang, Xiang Huang, Mei Wu, and Yue Meng "Supervised feature selection based on interclass distinctions for scene classification", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130903F (8 April 2024); https://doi.org/10.1117/12.3025675

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