Potential distribution prediction of three genus of Orthotrichaceae in Xinjiang based on MaxEnt niche model
ALANUR Kahar， WANG Pengjun， LU Yongman， YUAN Zhenyan， MAMTIMIN Sulayman
(Xinjiang Key Laboratory of Biological Resources and Genetic Engineering，College of Life Science and Technology， Xinjiang University， Urumqi 830046， China)
Abstract：The normalized digital surface model is an important feature to characterize the height of ground objects and assist in the classification of remote sensing images， but its flaky features and unstable precision restrict the improvement of classification accuracy. Aiming at this problem， this paper proposes a dual-path input semantic segmentation network considering the local normalized height. On the one hand， a dual-path input structure is designed to extract the spectral features and geometric features of the ground objects， and connect them through skip connections to fully learn the multi-modal information of ground objects. On the other hand， a new method of ground object height representation is proposed. Considering that deep neural network can only process images in a small area due to the limitation of GPU memory， the height features are calculated within the local area of the digital surface model. Finally， by comparing the three network frameworks on the ISPRS standard data set， it is demonstrated that the overall accuracy of the proposed method is improved by 4.5%～4.7% compared to the method using only spectral images， and the classification accuracy， computational efficiency and degree of automation are better than method with normalized digital surface model.
艾拉努尔·卡哈尔,王鹏军,逯永满,袁祯燕,买买提明·苏来曼. 基于MaxEnt生态位模型预测木灵藓科三属植物在新疆的潜在分布区[J]. 华中师范大学学报(自然科学版), 2022, 56(3): 487-496.
ALANUR Kahar,WANG Pengjun,LU Yongman,YUAN Zhenyan,MAMTIMIN Sulayman. Potential distribution prediction of three genus of Orthotrichaceae in Xinjiang based on MaxEnt niche model. journal1, 2022, 56(3): 487-496.
2022, Vol. 56 
