Abstract
Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since last decade. Generally, RoF uses principal component analysis (PCA) as the rotation tool, which is commonly acknowledged as an unsupervised feature extraction method, and does not consider the discriminative information about classes. Sometimes, however, it turns out to be sub-optimal for classification tasks. Therefore, in this paper, we propose an improved RoF algorithm, in which semi-supervised local discriminant analysis is used as the feature rotation tool. The proposed algorithm, named semi-supervised rotation forest (SSRoF), aims to take advantage of both the discriminative information and local structural information provided by the limited labeled and massive unlabeled samples, thus providing better class separability for subsequent classifications. In order to promote the diversity of features, we also adjust the semi-supervised local discriminant analysis into a weighted form, which can balance the contributions of labeled and unlabeled samples. Experiments on several hyperspectral images demonstrate the effectiveness of our proposed algorithm compared with several state-of-the-art ensemble learning approaches.
Highlights
Hyperspectral (HS) image classification always suffers from varieties of difficulties, such as high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels
Multiple classifier system (MCS), which is sometimes named as classifier ensemble or ensemble learning (EL) in the machine learning field, is a popular strategy for improving the classification performance of hyperspectral images by combining the predictions of multiple classifiers, thereby reducing the dependence on the performance of a single classifier [8,9,10,11]
It has been demonstrated that the performance of Local Fisher Discriminant Analysis (LFDA) tends to degrade if only a small number of labeled samples are available [40], while principal component analysis (PCA) or Neighborhood Preserving Embedding (NPE) (and other unsupervised feature extraction (FE) methods) will generally lose the discriminative information of labeled information
Summary
Hyperspectral (HS) image classification always suffers from varieties of difficulties, such as high dimensionality, limited or unbalanced training samples, spectral variability, and mixing pixels. Previous studies have demonstrated both theoretically and experimentally that one of the main reasons for the success of ensembles is the diversity among the individual learners (namely the base classifiers) [13], because combining similar classification results would not further improve the accuracy. A massive number of research studies show that RoF surpasses conventional RF due to the high diversity in training sample and features It is well documented in the literatures that PCA is not suitable for feature extraction (FE) in classification because it does not include discriminative information in calculating the optimal rotation of the axes [30,34,35]. In this paper, we present an improved ensemble learning method, which uses the semi-supervised feature extraction technique instead of PCA during the “rotation” process of classical RoF approach.
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