An EnFCM remote sensing image forest land extraction method based on PCA multi-feature fusion

The traditional EnFCM (Enhanced fuzzy C-means) algorithm only considers the grey-scale features in image segmentation, resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction. An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed. Firstly, histogram equalization was applied to improve the image contrast. Secondly, the texture and edge features of the image were extracted, and a multi-feature fused pixel image was generated using the PCA technique. Moreover, the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature. Finally, an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation. The experimental results showed that the error was between 1.5% and 4.0% compared with the forested area counted by experts’ hand-drawing, which could obtain a high accuracy segmentation and extraction result.