Watershed algorithm is widely used in image segmentation, but it has oversegmentation in image segmentation. Therefore, an image segmentation algorithm based on K-means and improved watershed algorithm is proposed. Firstly, Gaussian filter is used to denoise human skeleton image. K-means clustering algorithm is used to segment the denoised image and the connected component with the largest area is extracted as the initial human skeleton region. The initial bone region was morphologically opened and then morphologically closed to eliminate the noise. Morphologically open operation is used to disconnect other human tissues that adhere to the human bone region and eliminate the background noise with small area, while closed operation smoothes the edge of the human bone region and fills the fracture in the contour line. Secondly, the watershed segmentation algorithm is implemented on the image after morphological operation. The similarity degree of two blocks is defined according to the mean difference of gray level of adjacent blocks and the mean value of standard deviation of gray level of pixels in the edge of the block 4-neighborhood. The adaptive threshold T is generated by Otsu method for histogram of gradient amplitude image. If the similarity degree is greater than T, the image blocks will be merged; otherwise, the image blocks will not be merged. The proposed image segmentation algorithm is used to extract and segment the human bone region from 100 medical images containing human bone. The number of blocks segmented by watershed algorithm is 2775 to 3357, but the number of blocks segmented by the proposed algorithm is 221 to 559. The experimental results show that the proposed algorithm effectively solves the oversegmentation problem of watershed algorithm and effectively segments the image target.

No abstract

No abstract

Aiming at the problems that the traditional remote sensing image classification methods cannot effectively integrate a variety of deep learning features and poor classification performance, a land resource use classification method based on a convolutional neural network (CNN) in ecological remote sensing images is proposed. In this study, a seven-layer convolution neural network is constructed, and then the two fully connected layer features of the improved CNN network training output are fused with the fifth layer pooled layer features after dimensionality reduction by principal component analysis (PCA), so as to obtain an effective remote sensing image feature of land resources based on deep learning. Further, the classification of land resources remote sensing images is completed based on a support vector machine classifier. The remote sensing images of Pingshuo mining area in Shanxi Province are used to analyze the proposed method. The results show that the edge of the recognized image is clear, the classification accuracy, misclassification rate, and kappa coefficient are 0.9472, 0.0528, and 0.9435, respectively, and the model has excellent overall performance and good classification effect.

No abstract