Curve matching approach to object-based image analysis using spectral variability and spatial distributio

Authors: Fang Qiu*, University of Texas - Dallas, Yunwei Tang, Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences
Topics: Remote Sensing, Land Use, Quantitative Methods
Keywords: Object-based image analysis, curve matching, Spatial covariogram, Land cover/use classification
Session Type: Virtual Paper
Presentation File: No File Uploaded


Object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. Traditionally, object-level statistical summaries are usually used for classification, assuming spectral values within objects follow a Gaussian distribution, which may not be true because of within object spectral heterogeneity. Consequently, these statistical summaries may misrepresent the features of the object. This shortcoming is addressed in our study by integrating both the spectral variability and the spatial distribution of the pixels within objects to improve the traditional object-based image classification. The spectral variability is represented by histograms of the pixel values in the object, and the spatial distribution is characterized by the binary spatial covariogram of these pixels. To construct a binary spatial covariogram, a principal component analysis (PCA) is first applied to compress multiple bands into one, and the Otsu thresholding is then performed to generate a binary map reflecting the spatial configuration of the pixels. Spatial covariance is then computed for this binary map and plotted with different lag distances to derive the binary spatial covariogram. Our proposed model utilizing curves composed of the spectral histograms and binary spatial covariogram (referred to as the His-Cov model) are then used for classification based on curve matching approaches. The integration of spectral variability and spatial distribution of the pixels in the object produced superior results to curve matching approaches based on spectral variability alone and to traditional OBIA based on spectral and spatial features of the objects when classifying complex land use types in urban environments.

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