Deep Learning-Based Multiscale Pyramid Sieve and Analysis Module in Image Segmentation

Image segmentation is an essential part of remote sensing preprocessing. This paper proposes a deep learning-based multiscale pyramid sieve and analysis module to solve the problems associated with multiscale object coexistence in the complex semantic environments of multispectral remote sensing images. The image information distribution features, which change with scales in different convolutional feature map pyramids, were investigated, and the relationship between informative scales and the variation function curve was examined. Then, the proposed sieve and analysis module was designed according to the results. The proposed module is described as follows. (1) First, a feature map pyramid is built via a convolution calculation. (2) Sieve informative feature layers from the feature map pyramid with variation analysis. (3) Fuse informative feature layers. (4) The final feature representation is input into subsequent calculations for image segmentation. Experimental results demonstrate that, compared to the control group, the precision of the experimental group improved by 3.57%-5.89%, and the intraclass conformity improved by 2.74%-5.58%. In addition, the intraclass chaos decreased by 1.5%-7.9%. The experimental results demonstrate that the proposed multiscale pyramid sieve and analysis module can separate informative feature layers and improve the hierarchical segmentation accuracy of remote sensing multispectral images.