A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination

• Published in: Geocarto international 2023-12, Vol.38 (1)
• Main Authors: Zhang, Kaili, Chen, Yonggang, Wang, Wentao, Wu, Yudi, Wang, Bo, Yan, Yanting
• Format: Article
• Language: English
• Subjects: feature extraction and selection; Pixel Neighbourhood Similarity; Remote sensing image classification; spectral-spatial features
• ISSN: 1010-6049; 1752-0762
• DOI: 10.1080/10106049.2022.2158948

In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification.