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.

线性判别分析 (LDA) 是一种流行且简单的分类工具，通常优于遥感领域更复杂的现代机器学习技术。我们引入了一种新颖的 LDA 方法，该方法使用待分类对象的所有像素以及训练集中空间上接近的其他对象的空间自相关来提高分类性能。为了简化空间建模和模型拟合，该方法应用于变换后的特征向量。我们将此方法称为条件空间 LDA。与地统计插值中的通用克里金法非常相似，在条件空间 LDA 中结合使用特征数据和对标记训练数据进行调节能够最好地利用可用的地理空间数据。