Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Abstract

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results.

Keywords

• deep convolution neural network model
• super-pixel algorithm
• automatic fuzzy clustering
• prior entropy fuzzy c-means clustering algorithm
• remote sensing images