Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Classification Using Fuzzy Membership Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematics handbook for science and engineering
Mathematics handbook for science and engineering
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Optimizing templates for finding trees in aerial photographs
Pattern Recognition Letters
A fuzzy set-based accuracy assessment of soft classification
Pattern Recognition Letters
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Practical Handbook on Image Processing for Scientific Applications
Practical Handbook on Image Processing for Scientific Applications
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Digital Image Processing
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model
A fuzzy k-partitions model for categorical data and its comparison to the GoM model
Fuzzy Sets and Systems
International Journal of Remote Sensing - 3D Remote Sensing in Forestry
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This paper presents an application of fuzzy set theory for classification of individual tree crowns into species groups, in high spatial resolution colour infrared aerial photographs. In this type of digital image, the trees are visible as individual objects. The number of individuals to classify might be very large in the acquired set of photographs, but the applied grade of membership (GoM) model, which this paper focuses on, is suitable for dealing with large datasets.The extent of each tree crown in the image is defined using a previously published procedure. Based on colour information (hue), an optimal fuzzy thresholding technique divides the tree crown universal set into a dominant set and its minor complement. Nine different features of each image object are estimated, and transformed using principal component analysis (PCA). The first three or four PCs are subsequently used in the GoM model. Furthermore, the concept of fuzzy relation is applied to one of the descriptors: to predict a centroid of the star-shaped pattern of Norway spruce.The GoM model needs initial membership values, which are estimated using an unsupervised fuzzy clustering approach of small subareas (branches in the tree crowns) and their corresponding digital numbers in each colour band (RGB-images). The complete classification system comprises three independent components: decisions on coniferous/deciduous, Scots pine/Norway spruce, and Birch/Aspen. The accuracies (ground patches excluded), using the supervised GoM model with crossvalidation, are 87%, 76%, and 79%, respectively. The accuracy for the compounded system is 67%.