Open Access

ARTICLE

Remote Sensing Plateau Forest Segmentation with Boundary Preserving Double Loss Function Collaborative Learning

by Ying Ma¹, Jiaqi Zhang^2,3,4, Pengyu Liu^1,2,3,4,*, Zhihao Wei⁵, Lingfei Zhang¹, Xiaowei Jia⁶

1 School of Physics and Electronic Information Engineering, Qinghai Minzu University, Xining, 810007, China
2 The Information Department, Beijing University of Technology, Beijing, 100124, China
3 Advanced Information Network Beijing Laboratory, Beijing, 100124, China
4 Computational Intelligence and Intelligent Systems Beijing key Laboratory, Beijing, 100124, China
5 Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, 100871, China
6 Department of Computer Science, University of Pittsburgh, 15260, USA

* Corresponding Author: Pengyu Liu. Email:

Journal of New Media 2022, 4(4), 165-177. https://doi.org/10.32604/jnm.2022.026684

Received 05 November 2021; Accepted 03 April 2022; Issue published 12 December 2022

Abstract

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.

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