Cluster quality and an optimal number of clusters are the important issues in cluster analysis. The performance of seed based algorithms are dependent on initial cluster center selection and the optimal number of clusters in an unknown data set. To select the appropriate seed of a cluster is an important criterion of any seed based clustering technique. The growing point of the cluster is known as a seed. Experiments on two challenging datasets of remote sensing imagery show that the proposed method performs better than other models and can extract road information from complex scenes.Ĭlustering is one of the important unsupervised learning in data mining to group the similar features. In addition, we solve road disconnection issues in the results obtained with the neural network by extracting and analyzing the geometric structures and feature points of the roads. In the gradient descent process, a superior loss function is designed to solve the problem of class imbalance caused by road sparseness, and more attention is given to hard classification samples to extract narrow and covered roads. The network weights and biases of our proposed deep learning model are transmitted through the random combination of layers of different submodels during forward and backward propagation. In this paper, we propose a novel method for extracting roads using an ensemble learning model with a postprocessing stage. High-resolution satellite images contain valuable road semantic information, but the occlusion of vegetation and buildings and the sparse distribution and heterogeneous appearance of roads limit the accuracy of road extraction models. The method proposed in this paper has certain reference significance for the classification and repair of linear objects such as roads, power grids and tracks. Especially, the single broken road, has a high integrity of the road shape after repairing. The results show that the proposed method can better connect the roads formed by road or building shadows. In this paper, the images after road extraction based on the U-Net network is used to test the method. Secondly, use K-means clustering algorithm to search for road breakpoints, and eliminate invalid breakpoints then, fit the breakpoints of each category through polynomial curves, and record the mathematics of each fitted curve expression Finally, the coordinate sequences between each kind of breakpoint is calculated according to each fitted polynomial, and the corresponding pixel is filled with the width of the road to realize automatic detection and connection. The method extracts the road skeleton based on the binarized image after road extraction, and uses the eight neighborhood detection algorithm to find the road breakpoints after road extraction of high-resolution remote sensing image, and removes the isolated points of the road edge according to mathematical morphology filtering. Aiming at the problem of disconnection after road classification of remote sensing image, this paper proposes an optimization method for broken road connection considering spatial connectivity.