Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Abstract

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

“…Therefore it is of paramount importance to continuously monitor forests in order to understand the changes that occur with respect to time. Surveys were used to conduct forest monitoring, but such a technique is costly and cannot be completed in a short period of time [4].…”

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

“…Continuous monitoring of forest cover is therefore particularly essential. Forest cover monitoring can quantify important information such as forest area, cover, deforestation, and fire incidence, and provide a reliable basis for ecological monitoring [3]. At the same time, it is possible to understand the current situation and development trends of the forest ecological environment and to provide basic data for measures to protect it.…”