A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs

Li, Weijia; He, Conghui; Fu, Haohuan; Zheng, Juepeng; Dong, Runmin; Xia, Maocai; Yu, Le; Luk, Wayne

doi:10.3390/rs11091025

Cited by 26 publications

(14 citation statements)

References 39 publications

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“…The inclusion of multiple evaluation types is critical because each type of evaluation data has strengths and limitations in evaluating model performance. Field collected stems are the most common evaluation data used in crown detection work due to high confidence that each stem represents a location of a single tree [ 1 , 6 , 17 , 39 ]. However, the position of a tree stem can fail to accurately represent the position of the crown as viewed from above due to a combination of spatial errors in alignment with the image data and the tendency for trees to grow at acute angles (tree lean is not measured in the NEON data), such that the center of the crown and position of the stem can be offset by several meters.…”

Section: Discussionmentioning

confidence: 99%

“…location of a single tree [1,6,17,39]. However, the position of a tree stem can fail to accurately represent the position of the crown as viewed from above due to a combination of spatial errors in alignment with the image data and the tendency for trees to grow at acute angles (tree lean is not measured in the NEON data), such that the center of the crown and position of the stem can be offset by several meters.…”

Section: Plos Computational Biologymentioning

confidence: 99%

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A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

et al. 2021

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Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is to associate sensor data into individual crowns. While dozens of crown detection algorithms have been proposed, their performance is typically not compared based on standard data or evaluation metrics. There is a need for a benchmark dataset to minimize differences in reported results as well as support evaluation of algorithms across a broad range of forest types. Combining RGB, LiDAR and hyperspectral sensor data from the USA National Ecological Observatory Network’s Airborne Observation Platform with multiple types of evaluation data, we created a benchmark dataset to assess crown detection and delineation methods for canopy trees covering dominant forest types in the United States. This benchmark dataset includes an R package to standardize evaluation metrics and simplify comparisons between methods. The benchmark dataset contains over 6,000 image-annotated crowns, 400 field-annotated crowns, and 3,000 canopy stem points from a wide range of forest types. In addition, we include over 10,000 training crowns for optional use. We discuss the different evaluation data sources and assess the accuracy of the image-annotated crowns by comparing annotations among multiple annotators as well as overlapping field-annotated crowns. We provide an example submission and score for an open-source algorithm that can serve as a baseline for future methods.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

et al. 2021

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“…Recently, several approaches for tree detection used the local maximum filtering algorithm. For instance, Li et al [17] implemented a Field-Programmable Gate Array (FPGA) for the detection of tree crowns, speeding up the computations considerably without loss of performance. Xiao et al [18] used the DSM obtained from the 3D information provided by multiview satellite images to detect individual trees and delineate their crowns.…”

Section: Classical Tree Detectionmentioning

confidence: 99%

Assessment of Tree Detection Methods in Multispectral Aerial Images

et al. 2020

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Detecting individual trees and quantifying their biomass is crucial for carbon accounting procedures at the stand, landscape, and national levels. A significant challenge for many organizations is the amount of effort necessary to document carbon storage levels, especially in terms of human labor. To advance towards the goal of efficiently assessing the carbon content of forest, we evaluate methods to detect trees from high-resolution images taken from unoccupied aerial systems (UAS). In the process, we introduce the Digital Elevated Vegetation Model (DEVM), a representation that combines multispectral images, digital surface models, and digital terrain models. We show that the DEVM facilitates the development of refined synthetic data to detect individual trees using deep learning-based approaches. We carried out experiments in two tree fields located in different countries. Simultaneously, we perform comparisons among an array of classical and deep learning-based methods highlighting the precision and reliability of the DEVM.

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“…Rapid prototyping of high-complexity digital circuits is now possible thanks to the density of current programmable circuits, such as field programmable gate arrays (FPGAs) [1]. It is possible to quickly test the validity of new architectural concepts: the complete implementation of a processor on FPGAs circuits is today within our range, resulting in more evaluation possibilities than those offered by software simulators.…”

Section: Introductionmentioning

confidence: 99%

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

Makhloufi

Chekroun

Tagmouti

et al. 2021

IJECE

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The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.

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A Real-Time Tree Crown Detection Approach for Large-Scale Remote Sensing Images on FPGAs

Cited by 26 publications

References 39 publications

A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network

Assessment of Tree Detection Methods in Multispectral Aerial Images

Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm

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