COMPUTATIONAL TIME MODELLING FOR TREE CROWN EXTRACTION FROM HIGH RESOLUTION IMAGERY USING GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS

Abstract. Image segmentation, unlike most conventional approaches, assesses image pixel information in relation to image contextual information to extract congruent image objects. A comprehensive review of literature during the research conceptualization phase revealed that while a lot of work has been done into the optimization of the segmentation computational time, there was a conspicuous research gap in the area of computational time modelling and pre-analytical projections.

The focus of this research was, therefore, to model the computational time requirements of segmentation as a function of variability in image spatial resolutions and pixel counts. The employed methodology integrated structure from motion (SFM), segmentation, and regression modelling into a multiphase workflow to process UAV-acquired imagery into an orthomosaic, which underwent segmentation at multiple resolutions. The segmentation computational time was regressed against the image pixel count and spatial resolution.

The results showed that the image pixel count was the main determinant of the computational time, expressing a statistically strong linear relationship. However, when the regressors were tested individually, the spatial resolution showed an exponential relationship with computational time which, however, exhibited saturation. It can be inferred that image spatial resolution is not the key determinant of segmentation computational time but the pixel count of the image.

This research recommends the adoption of statistically viable models to thoroughly assess computational time requirements for image segmentation and a pre-analytics estimation of the time requirements such that image quality – image processing time adjustments can be made before rather than after analyses have commenced.