The Directional Neighborhoods Approach to Contextual Classification of Images From Noisy Data
提出一种有向邻域方法，通过观测数据选择最优非对称邻域来对含噪遥感图像中的像素进行分类和重建，蒙特卡洛模拟表明该方法显著优于参考贝叶斯上下文分类。
Abstract The directional neighborhoods approach (DNA) to classifying pixels and reconstructing images from remotely sensed noisy data is a newly proposed computer-intensive procedure that is partly Bayesian and partly data analytic. It uses the observational data to select an optimal, generally asymmetric, but relatively homogeneous neighborhood for contextually classifying pixels. A criterion for “homogeneity of neighborhood” is developed. DNA involves two stages: a zero-neighbor preclassification stage, followed by selection of the most homogeneous neighborhood, and then a final classification. We provide Monte Carlo simulations for a two-population image and compare DNA results with those from a reference Bayesian contextual classification. We show that DNA improves substantially on the reference classification procedure.