Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data

Franklin, Steven E.; Ahmed, Oumer S.

doi:10.1080/01431161.2017.1363442

Cited by 127 publications

(109 citation statements)

References 25 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…Considering the RF method applied to multispectral (MSP) data, Franklin and Ahmed [116] studied deciduous tree species classification using OBIA and RF. Both methods were combined for the classification, and 23 tree crowns (species: White birch, aspen, Sugar maple and Red maple) were used for validation.…”

Section: Tree Species Mapping and Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

View full text Add to dashboard Cite

Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

show abstract

Section: Tree Species Mapping and Classificationmentioning

confidence: 99%

“…Most of the studies concerning this topic focused on individual tree detection using different outcomes, then OBIA algorithms are applied [67,[116][117][118]121,123,124], creating a set of clusters. Properties of these clusters were then used to create datasets with different extracted parameters.…”

Section: Tree Species Mapping and Classificationmentioning

confidence: 99%

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Ruiz Hernandez and Shi [23] applied both GLCM texture metrics and spatial indices in a geographic OBIA framework with RF for urban land use mapping. Recently, Franklin and Ahmed [24] applied RF and object-based analysis for tree species classification on multispectral images captured by unmanned aerial vehicles (UAVs). Many studies have concluded that object-based spatial and textural features can significantly improve the classification [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

et al. 2019

View full text Add to dashboard Cite

Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.

show abstract

“…There exist tree detection methods that use multispectral or RGB cameras and specific descriptors such as crown size, crown contour, foliage cover, foliage color and texture [16]; while others rely on pixel-based classification techniques, such as calculating the Normalized Difference Vegetation Index (NDVI), Circular Hough Transform (CHT) and morphological operators to segment palm trees with an accuracy of 95% [17]. Other methods depend on object-based classification techniques; for example, they use the Random Forest algorithm on multispectral data with an accuracy value of 78% [18], or a naive Bayesian network on high-resolution aerial ortophotos and ancillary data (Digital Elevation Models and forest maps) with an accuracy value of 87% [19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Morales

Kemper

Sevillano

et al. 2018

Forests

View full text Add to dashboard Cite

One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512 × 512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.

show abstract

Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data

Cited by 127 publications

References 25 publications

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Contact Info

Product

Resources

About