An object-oriented multi-scale segmentation optimization algorithm based on PCA

Multi-scale segmentation is the basis of remote sensing images object-oriented classification. The paper proposes an object-oriented multi-scale segmentation optimization algorithm which combines dimension reduction technique with clustering algorithm aiming at the subjectivity of optimal segmentation scale determination of different regional features and the randomness of clustering center determined when using clustering algorithms. In this method, the initial clustering center is generated using the result of dimension reduction and sorting by Principal Component Analysis (PCA). Then the probability of merging each pixel is calculated by K-means clustering algorithm, so as to obtain the multi-scale segmentation results suitable for different scales in different research areas. This paper comparatively analyzes, in combination with the existing image segmentation methods and the original K-means algorithm, the K-means clustering segmentation after PCA dimension reduction, using multiple image databases, through a series of clustering evaluation indicators (internal and external evaluation indicators) and segmentation evaluation indicators (segmentation accuracy, over-segmentation rate and under-segmentation rate) to evaluate the result of different methods. The results show as follows: first, the method of the clustering algorithm after dimension reduction is more stable than the original clustering algorithm; second, compared with the traditional clustering algorithm, the PCA dimension reduction can identify the optimal segmentation scale more automatically; third, in the combination of dimension reduction technology and clustering algorithm, visual and quantitative evaluation indexes show that the clustering after dimension reduction preprocessing can get higher-quality segmentation results.