Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

Li, Zhengrong; Hayward, Ross; Zhang, Jinglan; Liu, Yuee; Walker, Rodney A.

doi:10.1109/icip.2009.5413642

Cited by 13 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of segmentation is, therefore, to detect and delineate all tree crowns from images while eliminating other image regions. We have developed an automatic tree crown detection and delineation algorithm by utilizing spectral features in a pulse coupled neural network followed by post-processing using morphological reconstruction [6]. Although the automatic segmentation is satisfied from visual assessment, decomposition of tree clusters is occasionally poor.…”

Section: Object-based Color and Texture Feature Extractionmentioning

confidence: 99%

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Liu

Hayward

et al. 2010

2010 IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

show abstract

Section: Object-based Color and Texture Feature Extractionmentioning

confidence: 99%

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Liu

Hayward

et al. 2010

2010 IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Considering the spectral properties of vegetation, particular NIR band can be very helpful to detect trees. Li et al employed a simplified pulse-coupled neural network (PCNN) that uses spectral features as input, postprocessed using morphological reconstruction (Li, Hayward, Zhang, Liu, & Walker, 2009). The algorithm has been shown to outperform both JSEG (Deng & Manjunath, 2001) and TreeAnalysis (Erikson, 2003) in tree crown segmentation, but the primary error source is the undersegmentation of tree clusters due to the crown overlap.…”

Section: Automated Data Processingmentioning

confidence: 99%

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

Bruggemann

Ford

et al. 2011

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor intensive, time consuming, and expensive. Remote sensing technologies represent an attractive and costeffective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing-based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments; we call this approach PTAGS (Power line Tracking Automatic Guidance System). Improved object recognition is achieved by fusing information from multisensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches. C 2011Wiley Periodicals, Inc.

show abstract

“…In this study, the algorithm developed by Li et al [23] is used for the detection and delineation of tree crowns, as it has been shown to outperform both JSEG [19] and TreeAnalysis [24] in these applications. The algorithm employs a simplified pulse-coupled neural network (PCNN) that uses spectral features as input, postprocessed using morphological reconstruction.…”

Section: A Vegetation Detectionmentioning

confidence: 99%

Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors

Mills

Castro

et al. 2010

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

Cited by 13 publications

References 11 publications

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

Evaluation of Aerial Remote Sensing Techniques for Vegetation Management in Power-Line Corridors

Contact Info

Product

Resources

About