Application Review of Convolutional Neural Network in Semantic Segmentation of Agricultural Remote Sensing Images
Convolution neural network is a special kind of artificial neural network,through convolution kernels traversal graph for characteristic information.In recent years,remote sensing technology has developed rapidly and been widely applied in the analysis of land use,crop classification recognition,crop growth monitoring and insect pests detection,convolution neural network provides a new method of extracting effective information from agricultural remote sensing images.The convolutional neural semantic segmentation network can annotate and segment remote sensing image pixels according to semantic information.Under the premise of gradual improvement of computer computing ability,the structure of the segmentation network is optimized,the depth is deepened,the segmentation accuracy is improved,and the performance is improved.In accordance with the need of actual network focus on the improvement of modular design,the analysis of land use and crop classification recognition applications,the improved network segmentation edge is thinning and clear,and pixel accuracy is higher.In terms of crop growth monitoring and plant diseases and insect pests identification,improvement allows modular network can efficiently implement segmentation task and meet the actual need,and the semantic segmentation of agricultural remote sensing image information by convolutional neural network provides information support for agricultural modernization and fine management.
万方数据
维普
