Spectral-spatial classification for hyperspectral imagery: a novel combination method based on affinity scoring

Abstract

Recently, a general framework for spectral-spatial classification has caught the attention of the hyperspectral imagery (HSI) society. It consists of three parts: classification, segmentation and combination of the former results to make a refined labeled map. Seeing the potentials of the last part, we derive a novel combination rule based on affinity scoring (CRAS). The core of the system is affinity score (AS), which is derived from fuzzy logic. Every AS measures the degree, i.e., the affinity, by which a pixel belongs to a class. The score is essentially decided by three factors: local spatial consistency, spectral similarity, and prior knowledge. The method is compatible with basic classification and segmentation tools, thus saving the trouble of designing complex techniques for the other parts in the framework. Experimental results show that CRAS excels several basic techniques as well as various state-of-the-art methods in the area of spectral-spatial classification.

摘要

创新点

近年来, 空谱结合的高光谱图像分类方法受到重视。 在该领域内, 存在一种分类-分割-合并的框架。 为了提升该框架中合并环节的作用, 本文提出一种新型的基于隶属度评分的新的合并方法, 用于空谱结合的分类。 该算法的核心是隶属度评分, 其概念由模糊数学中的 “隶属度” 发展而来。 一个隶属度分数实际上衡量的是一个像素属于某一个类别的程度。 隶属度评分主要受到三个因素的影响: 局部空间一致性, 光谱相似性以及先验知识。 提出的合并算法可与基本的经典的分类和分割算法结合使用。 实验证明提出的合并算法在分类精度上优于多种不同的空谱结合的经典算法及以当前先进的空谱结合的算法。