Identifying Tree Species From Bark David Gainer

Why Bark? Year-Round Can be used for dead trees/logs/stumps Can be observed at ground level Can be combined with systems for leafs/needles/twigs/etc.

Inherent Difficulties Age: Old Trees Have Very Different Looking Bark than Young Trees (So a complete system would have to identify tree and age range). Juvenile Bunya PineMature Bunya Pine Pictures from:

Inherent Difficulties Shape: Shape of the trunk effects the appearance of the bark.

Human Performance Osterreichische Bundesforste AG Dataset 2 Experts: 56.6% and 77.8% (trouble with the different pines) Source: Fiel and Sablatnig

Prior Results/Approaches Wan et al (2004) 77% with GLCM. Higher using color bands separately. 170 images of 9 classes Song et al (2004) 87.5% with GLCM and binary features. 180 images of 8 classes Huang et al (2006) 92.5% with GLCM and fractal dimensions. 360 images of 24 classes Fiel and Sablating (2011) 69.7% vectors of SIFT pattern matches images of 11 classes. Found that GLCM only yielded 61% for the same dataset

My Dataset Pictures of species in Central Park 2 Pictures for each individual tree Taken with iPhone, for each tree 1 picture using HDR setting, 1 with the standard Taken between 14” and 16” away from the tree Goal is 10 or more pictures for each species for 20 or more species Currently 4-14 pictures for 14 tree species

My Dataset Current Species: American Elm, London Plane, Red Oak, Eastern White Pine, Willow, Beech, Cherry (Kwanzan), Norway Maple, Birch (Himalayan Whitebark), Linden, American Sycamore, Blue Spruce, Sycamore Maple, Swamp Oak Identified with Help of Ned Barnard and Ken Chayas “Central Park Entire” map

Birch (Himalayan Whitebark) American Elm London Plane Beech Birch (Himalayan Whitebark)American Elm London PlaneBeech

Feature Selection From the prior research, I intend to implement GLCM, look at fractal dimensions I’d also like to come up with some new statistics especially from various qualities of edge images I’d also like to look at statistics derived from line detection

Feature Selection Gradient/Edge images in X and Y directions:

Classification Compare k-nn, multiclass SVM and maximum likelihood approaches