Canadian Forest Service Publications

Error reduction methods for local maximum filtering of high spatial resolution imagery for locating trees. 2002. Wulder, M.A.; Niemann, K.O.; Goodenough, D.G. Canadian Journal of Remote Sensing 28(5): 621-628.

Year: 2002

Available from: Pacific Forestry Centre

Catalog ID: 20722

Language: English

CFS Availability: Order paper copy (free), PDF (download)

Abstract

Tree crown recognition using high spatial resolution remotely sensed imagery provides useful information relating the number and distribution of trees in a landscape. a common technique used to identify tree locations uses a local maximum (LM) filter with a static-sized moving window. LM techniques operate on the assumption that high local radiance values represent the centroid of a tree crown. Although success has been found using LM techniques, various authors have noted the introduction of error through the inclusion of falsely identified trees. Missing trees, or omission error, are primarily the result of too coarse an image spatial resolution in relation to the size of the trees present. Falsely indicated trees (commission error) may be removed through image processing post-LM filtering. In this paper, using 1-m spatial resolution multi-detector electro-optical imaging sensor (MEIS-II) imagery of a study location on Vancouver Island, British Columbia, we present a variety of techniques for addressing commission error when applying an LM technique. Methods exploiting spatial and spectral information are applied. As a benchmark, LM generated within a 3 x 3 window with no commission error reduction resulted in a 67% overall accuracy, with a 22% commission error. The results of the commission error reduction must be considered against resultant overall accuracy. Using variable window sizes, as suggested by image spatial structure, for the generation of LM provided for the maintenance of similar overall accuracy (62%) with a decrease in commission error ( to 11%).

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