A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique

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

Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels

For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, termed as superpixel-based classification via multiple kernels (SC-MK). In HSI, each superpixel can be regarded as a shape-adaptive region which consists of a number of spatial-neighboring pixels with very similar spectral characteristics. Firstly, the proposed SC-MK method adopts an over-segmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information as well as spatial information within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machines classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods

Investigation of feature extraction algorithms and techniques for hyperspectral images.

Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.Hyperspectral images (HSIs) are remote-sensed images that are characterized by very high spatial and spectral dimensions and nd applications, for example, in land cover classi cation, urban planning and management, security and food processing. Unlike conventional three bands RGB images, their high dimensional data space creates a challenge for traditional image processing techniques which are usually based on the assumption that there exists su cient training samples in order to increase the likelihood of high classi cation accuracy. However, the high cost and di culty of obtaining ground truth of hyperspectral data sets makes this assumption unrealistic and necessitates the introduction of alternative methods for their processing. Several techniques have been developed in the exploration of the rich spectral and spatial information in HSIs. Speci cally, feature extraction (FE) techniques are introduced in the processing of HSIs as a necessary step before classi cation. They are aimed at transforming the high dimensional data of the HSI into one of a lower dimension while retaining as much spatial and/or spectral information as possible. In this research, we develop semi-supervised FE techniques which combine features of supervised and unsupervised techniques into a single framework for the processing of HSIs. Firstly, we developed a feature extraction algorithm known as Semi-Supervised Linear Embedding (SSLE) for the extraction of features in HSI. The algorithm combines supervised Linear Discriminant Analysis (LDA) and unsupervised Local Linear Embedding (LLE) to enhance class discrimination while also preserving the properties of classes of interest. The technique was developed based on the fact that LDA extracts features from HSIs by discriminating between classes of interest and it can only extract C 1 features provided there are C classes in the image by extracting features that are equivalent to the number of classes in the HSI. Experiments show that the SSLE algorithm overcomes the limitation of LDA and extracts features that are equivalent to ii iii the number of classes in HSIs. Secondly, a graphical manifold dimension reduction (DR) algorithm known as Graph Clustered Discriminant Analysis (GCDA) is developed. The algorithm is developed to dynamically select labeled samples from the pool of available unlabeled samples in order to complement the few available label samples in HSIs. The selection is achieved by entwining K-means clustering with a semi-supervised manifold discriminant analysis. Using two HSI data sets, experimental results show that GCDA extracts features that are equivalent to the number of classes with high classi cation accuracy when compared with other state-of-the-art techniques. Furthermore, we develop a window-based partitioning approach to preserve the spatial properties of HSIs when their features are being extracted. In this approach, the HSI is partitioned along its spatial dimension into n windows and the covariance matrices of each window are computed. The covariance matrices of the windows are then merged into a single matrix through using the Kalman ltering approach so that the resulting covariance matrix may be used for dimension reduction. Experiments show that the windowing approach achieves high classi cation accuracy and preserves the spatial properties of HSIs. For the proposed feature extraction techniques, Support Vector Machine (SVM) and Neural Networks (NN) classi cation techniques are employed and their performances are compared for these two classi ers. The performances of all proposed FE techniques have also been shown to outperform other state-of-the-art approaches

Spatial-Spectral Manifold Embedding of Hyperspectral Data

In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatial-spectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods

Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-supervised Hyperspectral Dimensionality Reduction

Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches