Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 801-808, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 801-808, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

COMPARITIVE STUDY OF TREE COUNTING ALGORITHMS IN DENSE AND SPARSE VEGETATIVE REGIONS

S. Khan1 and P. K. Gupta2 S. Khan and P. K. Gupta
  • 1Computer Science & Engineering Department, H.N.B. Garhwal University, Srinagar Garhwal, Pauri, India
  • 2Geoinformatics Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India

Keywords: Tree Crown Delineation, Morphological operators, Watershed algorithm, Color space, Convolution Neural Network

Abstract. Tree counting can be a challenging and time consuming task, especially if done manually. This study proposes and compares three different approaches for automatic detection and counting of trees in different vegetative regions. First approach is to mark extended minima’s, extended maxima’s along with morphological reconstruction operations on an image for delineation and tree crown segmentation. To separate two touching crowns, a marker controlled watershed algorithm is used. For second approach, the color segmentation method for tree identification is used. Starting with the conversion of an RGB image to HSV color space then filtering, enhancing and thresholding to isolate trees from non-trees elements followed by watershed algorithm to separate touching tree crowns. Third approach involves deep learning method for classification of tree and non-tree, using approximately 2268 positive and 1172 negative samples each. Each segment of an image is then classified and sliding window algorithm is used to locate each tree crown. Experimentation shows that the first approach is well suited for classification of trees is dense vegetation, whereas the second approach is more suitable for detecting trees in sparse vegetation. Deep learning classification accuracy lies in between these two approaches and gave an accuracy of 92% on validation data. The study shows that deep learning can be used as a quick and effective tool to ascertain the count of trees from airborne optical imagery.