Semisupervised collaborative representation graph embedding for hyperspectral imagery

In hyperspectral image (HSI) classification, feature extraction is one important step. Traditional methods, e.g., principal component analysis (PCA) and locality preserving projection, usually neglect the information of within-class similarity and between-class dissimilarity, which is helpful to the improvement of classification. On the other hand, most of these methods, e.g., PCA and linear discriminative analysis, consider that the HSI data lie on a low-dimensional manifold or each class is on a submanifold. However, some class data of HSI may lie on a multimanifold. To avoid these problems, we propose a method for feature extraction in HSIs, assuming that a local region resides on a submainfold. In our method, we deal with the data region by region by taking into account the different discriminative locality information. Then, under the metric learning framework, a robust distance metric is learned. It aims to learn a subspace in which the samples in the same class are as near as possible while the samples in different classes are as far as possible. Encouraging experimental results on two available hyperspectral data sets indicate that our proposed algorithm outperforms many existing feature extract methods for HSI classification.

Local sensitive discriminative broad learning system for hyperspectral image classification

We propose a semisupervised sparse graph learning (SSGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SSGL method aims to build a semisupervised sparse graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised sparse graph, we connect labeled samples according to their label information and sparse representation, connect unlabeled samples by sparse representation which set unlabeled training samples set as dictionary. Moreover, by setting sparse connections between labeled sample and unlabeled samples, the label information can be well propagated from labeled samples to unlabeled samples. In this way, the similarity of data points can be well modelled in the spectral feature space, and the data distribution characteristics can be better expressed. Experimental results on real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.

Recently, the semi-supervised feature extraction, which uses both a few labeled samples and a large quantity of unlabeled samples to improve discrimination of extracted features, has attracted attention in PolSAR data processing. However, most of the feature extraction methods are based on matrix operations and need to re-arrange the multiple features into vectors. Such a vectorization way may break the spatial structure and correlation among the neighboring pixels, which leads to the degradation of classification performance. To address this issue and incorporate the superiority of semi-supervised learning, a novel semi-supervised tensorial locally linear embedding (STLLE) method is proposed for PolSAR data. Each pixel and its spatial neighbors in a local window are represented as a third-order tensor, whose first two orders represent the neighborhood pixels, and the third order denotes the multiple features. The feature extraction is converted to find an optimal projection that can transform the high-dimensional features to low-dimensional ones. This can be solved by designing an objective function, in which the tensorial discriminant item includes the between-class and within-class scatter matrices on each mode of the labeled tensors. A regularizer is based on tensorial locally linear embedding graph using labeled and unlabeled tensor samples. Moreover, the pairwise class constraints are also introduced. Finally, we can use all the extracted features with a classifier such as K-Nearest Neighbor. Experimental results on three real PolSAR datasets demonstrate that STLLE significantly improves classification accuracies compared with state-of-the-art methods, which clearly verifies that the extracted features preserve the data structures well and are more discriminative.

Efficient dimensionality reduction can involve generative latent variable models such as probabilistic principal component analysis (PPCA) or independent component analysis (ICA). Such models aim to extract a reduced set of variables (latent variables) from the original ones. In most cases, the learning of these models occur within an unsupervised framework where only unlabeled samples are used. In this paper, we investigate the possibility of estimating an independent factor analysis model (IFA), and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. We propose to allow this model to learn within a semi-supervised framework in which likelihood of both labeled and unlabeled samples is maximized by a generalized expectation-maximization (GEM) algorithm. Experimental results with real data sets are provided to demonstrate the ability of our approach to find a low dimensional manifold with good explanatory power.

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

It is known that hyperspectral image (HSI) classification is a high-dimension low-sample-size problem. To ease this problem, one natural idea is to take the feature extraction as a preprocessing. A graph embedding model is a classic family of feature extraction methods, which preserves certain statistical or geometric properties of the data set. However, the graph embedding model considers only the pairwise relationship between two vertices, which cannot represent the complex relationships of the data. Utilizing the spatial structure of HSI, in this letter, we propose a spatial hypergraph embedding model for feature extraction. Experimental results demonstrate that our method outperforms many existing feature extract methods for HSI classification.

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new feature extraction method in this paper, which uses the boundary semi-labeled samples for solving small sample size problem. The proposed method, which called hybrid feature extraction based on boundary semi-labeled samples (HFE-BSL), uses a hybrid criterion that integrates both the local and global criteria for feature extraction. Thus, it is robust and flexible. The experimental results with three real hyperspectral images show the good efficiency of HFE-BSL compared to some popular and state-of-the-art feature extraction methods.

Due to the 3-D nature of hyperspectral images, as well as the spatial properties (such as regularity and continuity) of land covers, many 3-D feature extraction operators have been designed to fully exploit the joint spatial–spectral information. However, the large amount of obtained features can suffer from the “curse of dimensionality” problem, especially for the small training sample set. Moreover, various spatial–spectral features can represent the characteristics of the hyperspectral image from different aspects. In this paper, a multiple 3-D feature fusion framework (M3DF3) has been proposed for hyperspectral image classification. First, we extend the 2-D Gabor surface feature into 3-D (3DSF) domains to comply with the spatial–spectral structure of the hyperspectral image, which is directly applied on the original hyperspectral image instead of the Gabor features. Second, three 3-D feature extraction methods, including the 3-D morphological profile, the 3-D local binary pattern, and the proposed 3DSF, that, respectively, characterize the hyperspectral image from three different angles, i.e., morphology, local dependence, and shape smoothness, are fused under a multitask sparse representation framework to take full advantage of the multiple 3-D features together. The proposed M3DF3 approach was fully tested on three real-world hyperspectral image data, i.e., the widely used Indian Pines, Pavia University, and Houston University. The results show that our method can achieve as high as 68.22%, 79.44%, and 72.84% accuracies, respectively, even when only few samples, i.e., three samples per class, are used for training.

Feature extraction (FE) methods based on low-rank representation (LRR) have become important topics in hyperspectral images (HSIs) data analysis. In this paper, a supervised FE method for HSIs data based on LRR with the ability to preserve the local pairwise constraints information (LRLPC) is proposed. LRLPC does not change the data dimensionality and only employs a technique to enrich the original feature space (OFS) and to obtain enriched feature space, which results in features richer than OFS. To overcome the problem of LRR in lacking the local structure information (LSI) of data, a local discriminative regularization term is imposed on the fitness function of LRR to keep the LSI of data. For nonlinear structure of data, LRLPC is extended to kernel LRLPC (KLRLPC) using kernel trick. Utilization of existing information in the pairwise constraints is useful for limited labeled samples situations as a common problem in HSI data analysis. The obtained experimental results using two well-known HSI data sets confirm the effectiveness of LRLPC and KLRLPC for dimension reduction and classification of HSIs.

Deep learning based methods have recently been successfully explored in hyperspectral image classification field. However, training a deep learning model still requires a large number of labeled samples, which is usually impractical in hyperspectral images. In this paper, a simple but effective feature extraction method is proposed for hyperspectral image classification. Specifically, a pretrained deep convolutional neural network based on the ImageNet dataset is used to extract spatial features of a hyperspectral image. Recently, it is easy to obtain a pretrained convolutional neural network on the Internet. Note that the pretrained models are trained by using the ImageNet dataset. This means that the proposed method does not need labeled hyperspectral samples to train the deep model. Therefore, the proposed method alleviates the problem of lacking labeled samples and avoids the artificial design of feature extraction rules. Finally, the extracted features are stacked with spectral features as the input of a support vector machine classifier. The proposed method is conducted on three widely used hyperspectral image datasets. The experimental results demonstrate that the proposed method could outperform the conventional feature extraction methods and deep learning based methods.

Because of the character of hyperspectral remote sensing data,it is necessary and urgent to develop hyperspectral data process algorithm.In this paper,some hyperspectral data process algorithms for feature selection and feature extraction was discussed,and its advantage disadvantage was analyzed.In particular,we studied derivative spectral algorithm and put forward quad-encoding algorithm as the improved the binary encoding algorithm.Using the algorithms this paper proposed we extract spectral absorption parameter.The experiments have demonstrated that quad-encoding algorithm has the better performance than binary encoding on hyperspectral data,and for derivative spectrum it is effective to indicate validate feature for objects when its rank is higher.

This paper develops a supervised discriminant technique based on graph embedding, called maximum inter-class and marginal discriminant embedding (MIMDE) for feature and classification in high-dimensional data space. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible through local property (intra-class compactness graph and inter-class separability graph) and marginal property (margin separability graph). This characteristic makes MIMDE more intuitive and more powerful than marginal Fisher analysis which not considered inter-class separability graph. Experimental results on ORL and AR face databases show the effectiveness of the proposed method.

Hyperspectral image (HSI) classification remains a challenging problem due to unique characteristics of HSI data (such as numerous bands and strong correlations in the spectral and spatial domains) and small sample size. To address such concerns, we propose a novel spectral–spatial feature extraction method for HSI classification by employing graph embedding and deep learning (DL) models. Since the conventional graph cannot capture the complex manifold relationship of HSI data, and there exist the observations of within-class variation as well as the similarity between different classes in the spectral domain, we construct the supervised within-class/between-class hypergraph (SWBH) to extract the spectral features of HSI. Since it is difficult for DL models to learn representative features for HSI data when the labeled training samples are limited, we propose the random zero settings to newly generate a large amount of labeled HSI samples for the training of convolutional neural network (CNN). The designed sample expanded CNN (SECNN) is used to extract the HSI spatial features. Thus, the spectral–spatial features of HSI can be learned by integrating the features extracted from SWBH and SECNN, respectively. Experiments on three real HSI datasets demonstrate higher classification accuracy of the proposed SWBH–SECNN method.