Unsupervised spectral sub-feature learning for hyperspectral image classification

Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods

Optimized kernel minimum noise fraction transformation for hyperspectral image classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy

Low-Rank and Sparse Decomposition for Hyperspectral Image Enhancement and Clustering

In this dissertation, some new algorithms are developed for hyperspectral imaging analysis enhancement. Tensor data format is applied in hyperspectral dataset sparse and low-rank decomposition, which could enhance the classification and detection performance. And multi-view learning technique is applied in hyperspectral imaging clustering. Furthermore, kernel version of multi-view learning technique has been proposed, which could improve clustering performance. Most of low-rank and sparse decomposition algorithms are based on matrix data format for HSI analysis. As HSI contains high spectral dimensions, tensor based extended low-rank and sparse decomposition (TELRSD) is proposed in this dissertation for better performance of HSI classification with low-rank tensor part, and HSI detection with sparse tensor part. With this tensor based method, HSI is processed in 3D data format, and information between spectral bands and pixels maintain integrated during decomposition process. This proposed algorithm is compared with other state-of-art methods. And the experiment results show that TELRSD has the best performance among all those comparison algorithms. HSI clustering is an unsupervised task, which aims to group pixels into different groups without labeled information. Low-rank sparse subspace clustering (LRSSC) is the most popular algorithms for this clustering task. The spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC) algorithms is proposed in this dissertation, which extended LRSSC with multi-view learning technique. In this algorithm, spectral and spatial views are created to generate multi-view dataset of HSI, where spectral partition, morphological component analysis (MCA) and principle component analysis (PCA) are applied to create others views. Furthermore, kernel version of SSMLC (k-SSMLC) also has been investigated. The performance of SSMLC and k-SSMLC are compared with sparse subspace clustering (SSC), low-rank sparse subspace clustering (LRSSC), and spectral-spatial sparse subspace clustering (S4C). It has shown that SSMLC could improve the performance of LRSSC, and k-SSMLC has the best performance. The spectral clustering has been proved that it equivalent to non-negative matrix factorization (NMF) problem. In this case, NMF could be applied to the clustering problem. In order to include local and nonlinear features in data source, orthogonal NMF (ONMF), graph-regularized NMF (GNMF) and kernel NMF (k-NMF) has been proposed for better clustering performance. The non-linear orthogonal graph NMF combine both kernel, orthogonal and graph constraints in NMF (k-OGNMF), which push up the clustering performance further. In the HSI domain, kernel multi-view based orthogonal graph NMF (k-MOGNMF) is applied for subspace clustering, where k-OGNMF is extended with multi-view algorithm, and it has better performance and computation efficiency

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201

Feature extraction and classification for hyperspectral remote sensing images

Recent advances in sensor technology have led to an increased availability of hyperspectral remote sensing data at very high both spectral and spatial resolutions. Many techniques are developed to explore the spectral information and the spatial information of these data. In particular, feature extraction (FE) aimed at reducing the dimensionality of hyperspectral data while keeping as much spectral information as possible is one of methods to preserve the spectral information, while morphological profile analysis is the most popular methods used to explore the spatial information. Hyperspectral sensors collect information as a set of images represented by hundreds of spectral bands. While offering much richer spectral information than regular RGB and multispectral images, the high dimensional hyperspectal data creates also a challenge for traditional spectral data processing techniques. Conventional classification methods perform poorly on hyperspectral data due to the curse of dimensionality (i.e. the Hughes phenomenon: for a limited number of training samples, the classification accuracy decreases as the dimension increases). Classification techniques in pattern recognition typically assume that there are enough training samples available to obtain reasonably accurate class descriptions in quantitative form. However, the assumption that enough training samples are available to accurately estimate the class description is frequently not satisfied for hyperspectral remote sensing data classification, because the cost of collecting ground-truth of observed data can be considerably difficult and expensive. In contrast, techniques making accurate estimation by using only small training samples can save time and cost considerably. The small sample size problem therefore becomes a very important issue for hyperspectral image classification. Very high-resolution remotely sensed images from urban areas have recently become available. The classification of such images is challenging because urban areas often comprise a large number of different surface materials, and consequently the heterogeneity of urban images is relatively high. Moreover, different information classes can be made up of spectrally similar surface materials. Therefore, it is important to combine spectral and spatial information to improve the classification accuracy. In particular, morphological profile analysis is one of the most popular methods to explore the spatial information of the high resolution remote sensing data. When using morphological profiles (MPs) to explore the spatial information for the classification of hyperspectral data, one should consider three important issues. Firstly, classical morphological openings and closings degrade the object boundaries and deform the object shapes, while the morphological profile by reconstruction leads to some unexpected and undesirable results (e.g. over-reconstruction). Secondly, the generated MPs produce high-dimensional data, which may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. Last but not least, linear features, which are used to construct MPs, lose too much spectral information when extracted from the original hyperspectral data. In order to overcome these problems and improve the classification results, we develop effective feature extraction algorithms and combine morphological features for the classification of hyperspectral remote sensing data. The contributions of this thesis are as follows. As the first contribution of this thesis, a novel semi-supervised local discriminant analysis (SELD) method is proposed for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (Local Linear Feature Extraction Methods (LLFE)) and supervised method (Linear Discriminant Analysis (LDA)) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Our second contribution is the application of morphological profiles with partial reconstruction to explore the spatial information in hyperspectral remote sensing data from the urban areas. Classical morphological openings and closings degrade the object boundaries and deform the object shapes. Morphological openings and closings by reconstruction can avoid this problem, but this process leads to some undesirable effects. Objects expected to disappear at a certain scale remain present when using morphological openings and closings by reconstruction, which means that object size is often incorrectly represented. Morphological profiles with partial reconstruction improve upon both classical MPs and MPs with reconstruction. The shapes of objects are better preserved than classical MPs and the size information is preserved better than in reconstruction MPs. A novel semi-supervised feature extraction framework for dimension reduction of generated morphological profiles is the third contribution of this thesis. The morphological profiles (MPs) with different structuring elements and a range of increasing sizes of morphological operators produce high-dimensional data. These high-dimensional data may contain redundant information and create a new challenge for conventional classification methods, especially for the classifiers which are not robust to the Hughes phenomenon. To the best of our knowledge the use of semi-supervised feature extraction methods for the generated morphological profiles has not been investigated yet. The proposed generalized semi-supervised local discriminant analysis (GSELD) is an extension of SELD with a data-driven parameter. In our fourth contribution, we propose a fast iterative kernel principal component analysis (FIKPCA) to extract features from hyperspectral images. In many applications, linear FE methods, which depend on linear projection, can result in loss of nonlinear properties of the original data after reduction of dimensionality. Traditional nonlinear methods will cause some problems on storage resources and computational load. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which estimates the eigenvectors through iteration. Without performing eigen decomposition on the Gram matrix, our approach can reduce the space complexity and time complexity greatly. Our last contribution constructs MPs with partial reconstruction on nonlinear features. Traditional linear features, on which the morphological profiles usually are built, lose too much spectral information. Nonlinear features are more suitable to describe higher order complex and nonlinear distributions. In particular, kernel principal components are among the nonlinear features we used to built MPs with partial reconstruction, which led to significant improvement in terms of classification accuracies. The experimental analysis performed with the novel techniques developed in this thesis demonstrates an improvement in terms of accuracies in different fields of application when compared to other state of the art methods