Characterizing Tree Species of a Tropical Wetland in Southern China at the Individual Tree Level Based on Convolutional Neural Network

Sun, Ying; Xin, Qinchuan; Huang, Jianfeng; Huang, Bo; Zhang, Hongsheng

doi:10.1109/jstars.2019.2950721

Cited by 35 publications

(28 citation statements)

References 40 publications

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“…Despite these works, more research is need, as there are no significant studies about this topic at ground level which focus on the detection of tree trunks with deep learning models and their evaluation with well-known metrics in the object detection domain. Furthermore, the majority of works related to forest tree detection are focused on performing the detection with Light Detection and Ranging (LiDaR) data alone [ 26 , 27 , 28 , 29 , 30 ], with aerial high-resolution multispectral imagery alone [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] or with a combination of both [ 41 , 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

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Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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“…Furthermore, consideration needs to be given for changes that are not linked to urbanised land cover change, such as seasonal vegetation variations. In addition, some works, including other spatial data, have been investigated in order to overcome the sub-pixel mixing problem and improve per-pixel compartmentalisation by using textural [55] and contextual [56] information.…”

Section: Urban Change Detectionmentioning

confidence: 99%

Detection of Urban and Environmental Changes via Remote Sensing

2021

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Rapid climate and environmental change at limited, regional, and general scales have been a major concern for researchers in a number of fields, such as topography, economy, environment, and sustainable development. Changes in land cover and land use have taken into account due to potential impacts on soil depletion, amplified runoff , water balance, and climate change. A detailed understanding of the characteristics of land exploitation and land structure is indispensable for the study of their influences on life and nature. In addition, urban extension is a major form of land extraction and land transformation, as it relates to the rise in population and the availability of financial services. Remote sensing records have been shown to be important for reporting and perceiving urban development and transition, and for providing critical information for future growth. Transformation and shift identification are the tools used to recognise distinctions in a land cover by tracking them at various times. In addition, various change identification and detection approaches are routinely tested with the goal of providing the greatest change detection deductions for a particular appliance. This review would aim to establish a practical plan that combines remote sensing techniques, on the one hand, and modelling approaches, on the other, to track land use, to cover changes, and to predict future trends.

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“…In another example, a CNN regression was proposed to develop a model applicable to hyperspectral imagery for estimation of concentrations of phycocyanin and chlorophyll-a [52]. CNNs also have been used in OpenStreetMap Data Quality Assessment [53], oil spill segmentation [54], ship position detection and direction prediction [55], multimodal RS image registration [56], road extraction [57], and many other areas of study [58][59][60].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Ghanbari

Mahdianpari

Homayouni

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This paper presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including 1) general characteristics such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

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Characterizing Tree Species of a Tropical Wetland in Southern China at the Individual Tree Level Based on Convolutional Neural Network

Cited by 35 publications

References 40 publications

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Detection of Urban and Environmental Changes via Remote Sensing

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

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