Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

Liu, Yang; Zhang, Huaiqing; Lei, Kexin; Zuo, Yuanqing; Wang, Jiansen; Hu, Xingtao; Qiu, Hanqing

doi:10.3390/rs15020519

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…High-spatial-resolution remote sensing data have shown great potential for application in areas such as precision agricultural monitoring [1][2][3], urban and rural regional planning, road traffic management [4,5], high precision navigation maps [6][7][8], environmental disaster assessment [9,10], forestry measurement [11][12][13], and military construction. Buildings, as the main body in urban construction, occupy a more important component in highresolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

Wang,

Lin,

Huang

et al. 2023

Remote Sensing

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Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.

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Section: Introductionmentioning

confidence: 99%

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

Wang,

Lin,

Huang

et al. 2023

Remote Sensing

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“…As such, the development of a tree trunk detection framework or method adaptable to forest scenarios has become pressing. After all, tree trunk detection lays the groundwork for calculating forest resources, such as DBH [20], volume estimation, and individual tree segmentation [21].…”

Section: Introductionmentioning

confidence: 99%

“…Computers based on deep learning technology possess strong memory and learning capabilities [16,[20][21][22][23], effectively addressing the limitations of traditional DBH estimation methods in extracting forest DBH information, such as their difficulty in point cloud noise recognition, insufficient robustness, and low extraction efficiency. By learning trunk features, repeatable and scalable trunk recognition and localization tasks with large amounts of data can be achieved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm

Zhang

et al. 2023

Remote Sensing

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Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC’s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF’s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.

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“…The MAST [3] model takes advantage of the adaptive spatial nature of superpixels to achieve better classification performance with high-resolution remotely sensed images. With these applications of superpixel segmentation algorithms [4][5][6][7][8][9][10], superpixel segmentation has become a key technology in the remote sensing of the computer vision field.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation

Liu,

Zhong,

et al. 2023

Applied Sciences

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A superpixel is a group of pixels with similar low-level and mid-level properties, which can be seen as a basic unit in the pre-processing of remote sensing images. Therefore, superpixel segmentation can reduce the computation cost largely. However, all the deep-learning-based methods still suffer from the under-segmentation and low compactness problem of remote sensing images. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor is used to extract the multi-scale superpixel feature with contextual information. The multi-scale superpixel feature with contextual information can solve the low compactness effectively. The self-dynamic gate network introduces the gating and dynamic mechanisms to inject detailed information, which solves the under-segmentation effectively. Massive experiments have shown that our EAGNet can achieve the state-of-the-art performance between k-means and deep-learning-based methods. Our methods achieved 97.61 in ASA and 18.85 in CO on the BSDS500. Furthermore, we also conduct the experiment on the remote sensing dataset to show the generalization of our EAGNet in remote sensing fields.

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Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection

Cited by 6 publications

References 28 publications

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm

Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation

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