Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the r...Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the remote sensing technic,large-scale satellite data become available for surface monitoring.Due to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges.Recent developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing segmentation.However,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high robustness.Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative learning.There are three improvements in this article.First,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the input.Second,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss function.Third,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training.We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve.The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorithms.As a result of this investigation,the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.展开更多
基金supported by the following funds:Basic Research Program of Qinghai Province under Grants No.2020-ZJ-709National Key R&D Program of China (2018YFF01010100)+1 种基金Natural Science Foundation of Beijing (4212001)Advanced information network Beijing laboratory (PXM2019_014204_500029).
文摘Plateau forest plays an important role in the high-altitude ecosystem,and contributes to the global carbon cycle.Plateau forest monitoring request in-suit data from field investigation.With recent development of the remote sensing technic,large-scale satellite data become available for surface monitoring.Due to the various information contained in the remote sensing data,obtain accurate plateau forest segmentation from the remote sensing imagery still remain challenges.Recent developed deep learning(DL)models such as deep convolutional neural network(CNN)has been widely used in image processing tasks,and shows possibility for remote sensing segmentation.However,due to the unique characteristics and growing environment of the plateau forest,generate feature with high robustness needs to design structures with high robustness.Aiming at the problem that the existing deep learning segmentation methods are difficult to generate the accurate boundary of the plateau forest within the satellite imagery,we propose a method of using boundary feature maps for collaborative learning.There are three improvements in this article.First,design a multi input model for plateau forest segmentation,including the boundary feature map as an additional input label to increase the amount of information at the input.Second,we apply a strong boundary search algorithm to obtain boundary value,and propose a boundary value loss function.Third,improve the Unet segmentation network and combine dense block to improve the feature reuse ability and reduces the image information loss of the model during training.We then demonstrate the utility of our method by detecting plateau forest regions from ZY-3 satellite regarding to Sanjiangyuan nature reserve.The experimental results show that the proposed method can utilize multiple feature information comprehensively which is beneficial to extracting information from boundary,and the detection accuracy is generally higher than several state-of-art algorithms.As a result of this investigation,the study will contribute in several ways to our understanding of DL for region detection and will provide a basis for further researches.
