Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs

Abstract

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan.

Mapping the Distribution of High-Value Broadleaf Tree Crowns through Unmanned Aerial Vehicle Image Analysis Using Deep Learning

“…In forestry, where objects of interest may vary in size, it is essential to have a network that can understand features at different levels of granularity. The U-Net architecture allows it to capture features at multiple scales through its encoder and decoder [16]. Moreover, the U-Net incorporates skip connections that can preserve spatial information during down-sampling and up-sampling steps [36], which is important for accurately delineating the boundaries of individual trees.…”

“…Among the many deep learning models, U-Net has demonstrated state-of-the-art performance in multiple image segmentation tasks in the forestry sector, including forest change detection, post-forest-fire monitoring, forest type classification, and the mapping of tree species distribution [16][17][18][19][20]. However, the U-Net architecture, like many deep neural networks, may suffer from the vanishing gradient problem, which can make the models less capable of capturing fine details and distinguishing borders between different objects or classes.…”

Fine Classification of Urban Tree Species Based on UAV-Based RGB Imagery and LiDAR Data

“…However, when it comes to urban environments, both pixel-based classification methods and object-based classification methods face specific challenges. In the case of pixel-based classification methods, processing urban imagery can sometimes result in noticeable salt-and-pepper effects [22,43]. In addition, object-based classification methods struggle to accurately identify and extract individual trees as objects in densely forested urban areas.…”

MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

“…Lately, deep learning has greatly promoted the development of the computer vision field [17,18]. By leveraging neural networks to automatically discover more abstract features from input data, deep learning reduces the incompleteness caused by handcrafted features, leading to more competitive performance compared to traditional methods.…”