Object-Based Image Analysis Using Harmonic Analysis on A High-Spatial Resolution Satellite Image

The ability to extract biophysical and structural properties of features in digital images is an important goal in remote sensing. Delineating the boundaries of image features, and building meaningful numerical descriptors of them based on their geometric shape, are both challenging tasks that are necessary to identify shape pattern in image data. This study seeks to evaluate and compare the performance of Fourier harmonic descriptors against dimensionless ratios of image-object shape for characterizing objects in image classification training samples. We took 150 random samples of three different object types - forest canopies, residential houses, and buildings (50 samples each) - from a high spatial-resolution satellite image (WorldView-2) covering a portion of the southern end of California's Great Central Valley. To identify patterns in object shapes and perform object classification, we followed three steps. First, we performed image segmentation through an object-based image analysis (OBIA) method based on the Watershed Transform. We then carried out shape characterization using Fourier harmonics to measure variation in the silhouette of different object types. Finally, we compared and evaluated the performance of dimensionless ratios with Fourier descriptors by classifying different object shapes and assessing the accuracy of each method. Multinomial regression models were fitted to compare the accuracy and error of the two methods. Classification accuracy assessment was addressed utilizing hypothesis tests and significance of the likelihood ratio (LR) test and Akaike's Information Criterion (AIC). Dimensionless ratios and Fourier harmonics had similar, moderate accuracy of 70% and 73.33%, respectively, but only four harmonics were required to achieve the best model fit, and six were needed for dimensionless ratios. Harmonic analysis provides quantitative descriptions of object shapes allowing pattern characterizations that can improve a supervised classification.