Spatial linear discriminant analysis approaches for remote-sensing classification

Abstract

Linear Discriminant Analysis (LDA) is a popular and simple classification tool that often outperforms more sophisticated modern machine learning techniques in remote sensing. We introduce a novel LDA method that uses spatial autocorrelation of all pixels of an object to be classified but also of other objects of the training set that are spatially close to improve classification performance. To simplify spatial modelling and model fitting, the methodology is applied to the transformed feature vectors. We term this method conditional spatial LDA. Much alike universal Kriging in geostatistical interpolation, the combined use of feature data and conditioning on labelled training data in conditional spatial LDA was best able to exploit the available geospatial data. The method is illustrated on a crop classification case study from the Aconcagua agricultural region in central Chile.