Geographic object-based image analysis (GEOBIA) has been commonly applied for land cover and land use (LCLU) mapping and change identification analyses, particularly in the era of greater availability and affordability of finer spatial resolution remote sensing data. Conventional GEOBIA techniques classify image objects based on statistical measurements assuming within-object pixels are normally distributed. Although a few studies have found that within-object pixels tend to be non-normally distributed and tested histogram curve matching approaches for object-based classification, no studies have tested classification of temporal-spectral object pixels for the purpose of identifying land use change transitions. The context for this study is updating extant LU GIS layers that become out of date as a result of urban growth. The objectives of the study are to develop, test and compare GEOBICA techniques based on two histogram classifiers and a nearest neighbor (NN) classifier. The information content of frequency distributions of pixels within LU change / no-change objects was evaluated for different feature inputs and classifiers within a southern San Diego study area. The results demonstrate that histogram classifiers consistently outperformed a traditional NN classifier for the majority of feature input combinations. The most accurate combination (i.e., Histogram Matching Root Sum Squared Differential Area (HMRSSDA)) with temporal-spectral difference bands and arithmetic mean) yielded 79.82% overall accuracy, 78.72% and 81.07% for change and no-change objects, respectively.
