Abstract: Taking Wuyi Mountain National Park in Fujian Province as the study area and three types of natural forests, including Pinus massoniana,Fir and broadleaf mixed forests, CA-Net, an automatic canopy extraction method based on deep learning combined with coordinated attention mechanism was proposed.The results show that:（1） the Watershed algorithm was poorly applicable in the natural forest dataset, whereas the mIoU,mPA and Accuracy of CA-Net is improved comPAred with U-Net, improved by 4.75%,3.27% and 2.84%,respectively.（2） On the background category, CA-Net′s IoU,Recall,and Precision reached 69.58%,84.17% and 80.06%,respectively; whereas on the canopy category, CA-Net′s IoU,Recall and Precision were 83.85%,90.11% and 92.35%,respectively.（3） CA-Net had the highest segmentation Accuracy on mixed fir-broadleaf forests, and the segmentation Accuracy mIoU increased by 1.83% and 3.12%,mPA increased by 6.93% and 2.95%,Accuracy increased by 3.57% and 1.83%,respectively, comPAred with that of U-Net for mixed Masson pine-fir forests, and mixed Masson pine-fir-broadleaf forests.It can be seen that CA-Net can effectively overcome the problems of background interference and difficulties in fine feature extraction to improve the accuracy and efficiency of canopy segmentation in complex natural forests.