IEEE Trans Image Process. 2010 Nov;19(11):2983-99. doi: 10.1109/TIP.2010.2051632. Epub 2010 Jun 1.
This paper presents a novel support vector machine classifier designed for sub-pixel image classification (pixel/spectral unmixing). The proposed classifier generalizes the properties of SVMs to the identification and modeling of the abundances of classes in mixed pixels by using fuzzy logic. This results in the definition of a fuzzy-input fuzzy-output support vector machine (F2SVM) classifier that can: i) process fuzzy information given as input to the classification algorithm for modeling the sub-pixel information in the learning phase of the classifier, and ii) provide a fuzzy modeling of the classification results, allowing a relation many-to-one between classes and pixels. The presented binary F2SVM can address multicategory problems according to two strategies: the fuzzy one-against-all (FOAA) and the fuzzy one-against-one strategies (FOAO). These strategies generalize to the fuzzy case techniques based on ensembles of binary classifiers used for addressing multicategory problems in crisp classification problems. The effectiveness of the proposed F2SVM classifier is tested on three problems related to image classification in presence of mixed pixels having different characteristics. Experimental results confirm the validity of the proposed sub-pixel classification method.
本文提出了一种新颖的支持向量机分类器，用于子像素图像分类（像素/光谱混合分解）。所提出的分类器通过使用模糊逻辑将 SVM 的特性推广到混合像素中类别的丰度的识别和建模。这导致定义了模糊输入模糊输出支持向量机（F2SVM）分类器，该分类器能够：i）处理作为分类算法输入的模糊信息，用于在分类器的学习阶段对亚像素信息进行建模，以及 ii）提供分类结果的模糊建模，允许类和像素之间存在多对一的关系。所提出的二进制 F2SVM 可以根据两种策略解决多类别问题：模糊一对所有（FOAA）和模糊一对一策略（FOAO）。这些策略将基于用于解决硬分类问题中多类别问题的二进制分类器集合的技术推广到模糊情况。所提出的 F2SVM 分类器在三个与存在混合像素的图像分类相关的问题上进行了测试，这些问题具有不同的特征。实验结果证实了所提出的亚像素分类方法的有效性。
