Search

Reliable labelled samples have always played a vital role in the supervised paradigm of hyperspectral imagery (HSI) classification due to the fact that the inclusion of incorrect label information in the training set can seriously degrade the performance of classification methods. Recently, although some inter-class difference-based detection algorithms have been developed to remove mislabelled samples (i.e. noisy labels) in training set, the benefit of contextual information for each sample has not been fully explored yet. In this paper, a training redefinition with entropy-based structure set density (ESSD) method is designed, which consists of following main steps. First, the proposed ESSD method employs an over-segmentation technique to cluster the HSI into many shape-adaptive regions that correspond to sample sets. Then, each sample set is represented with an affine hull (AH) model, which exploits both the similarity and variance of samples within each sample set to adaptively characterize the set. Specifically, considering spectral and spatial weak assumptions among samples in each sample set, the idea of entropy trick-based k -nearest neighbour is introduced into each sample set to redefine its structure by removing different class from the sample set. Next, the distance among AH corresponding to each training sample is calculated based on the AH model. Meanwhile, the set-to-set distance is fed to the density peak algorithm to obtain the density of training samples. Finally, a decision-making value is applied to the density of each training sample to cleanse mislabelled samples within noisy training set. Experimental results on real HSI date sets demonstrate the superiority of the proposed method over several well-known training redefinition methods in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is a challenging problem for machine learning methods due to the small number of labelled samples and high spectral variability. In this paper, to solve this problem, a novel superpixel and multi-classifier fusion (SMCF)-based classification method for hyperspectral images is proposed. This method takes full advantage of the spectral information of superpixels and the spatial information of hyperspectral images and includes the following three steps. First, superpixels are used to increase the number of training samples and their spectral diversity. Second, label propagation (LP) is used to classify the hyperspectral images. Although LP is an efficient semi-supervised classification method, the corresponding performance is poor for certain land cover types with dispersed spatial distributions. Thus, a support vector machine (SVM) classifier is introduced to classify the hyperspectral images. Finally, the results of the SVM and LP classifiers are combined using our new class-specific weighted fusion algorithm. In the experiments, we selected three widely used and real hyperspectral data sets for evaluation. The final classification performance was evaluated based on two common metrics: the overall accuracy (OA) and the Kappa coefficient. The experimental results show that the proposed SMCF method is superior to six well-known classification methods, even when only 1% or less of the labelled samples are used. [ABSTRACT FROM AUTHOR]

An unsupervised feature extraction method could effectively address the phenomenon where using a limited number of labelled samples leads to poor hyperspectral image (HSI) classification. Inspired by this method, we have developed a new method based on fusing dual unsupervised features for HSI classification in this paper. First, we used principal component analysis (PCA) to reduce the dimensions of HSI data and obtain the first set of unsupervised features. Then, we got the second set of unsupervised features by adopting the self-taught learning method, which could learn more discriminatory features by making full use of the information of unlabelled samples in the same HSI. Next, we used the correlation canonical analysis (CCA) algorithm to fuse linearly dual unsupervised learning features. Finally, we proved the effectiveness of the features extracted by the proposed algorithm by applying the extreme learning machine (ELM) classifier to evaluate the purposes. Experiments with three widely used real HSI datasets showed good classification performance when the number of training samples was quite limited. This demonstrates that the proposed feature learning method was indeed an effective method for HSI classification. [ABSTRACT FROM AUTHOR]