Spectral-spatial Classification Of Hyperspectral Images Research Articles

Hyperspectral Image Classification With Pre-Activation Residual Attention Network

Recently, convolutional neural networks (CNNs) have been introduced for hyperspectral image (HSI) classification and shown considerable classification performance. However, the previous CNNs designed for spectral-spatial HSI classification lay stress on the learning for the spatial correlation of HSI data and neglect the channel responses of feature maps. Furthermore, the lack of training samples remains the major challenge for CNN-based HSI classification methods to achieve better performance. To address the aforementioned issues, this paper proposes a new end-to-end pre-activation residual attention network (PRAN) for HSI classification. The pre-activation mechanism and attention mechanism are introduced into the proposed network, and a pre-activation residual attention block (PRAB) is designed, which allows the proposed network to carry adaptively feature recalibration of channel responses and learn more robust spectral-spatial joint feature representations. The proposed PRAN is equipped with two PRABs and several convolutional layers with different kernel sizes, which enables the PRAN to extract high-level discriminative features. Experimental results on three benchmark HSI datasets reveal that the proposed method is provided with competitive performance over several state-of-the-art HSI classification methods, especially when the training set size is relatively small.

Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks

ABSTRACTDeep neural networks have recently been successfully explored to extract deep features for hyperspectral image classification. Recurrent neural networks (RNNs) are an important branch of the deep learning family, which are widely used for sequence analysis. Indeed, RNNs have been used to model the dependencies between the different spectral bands of hyperspectral image, inspired by the observation that hyperspectral pixels can be considered as spectral sequences. A disadvantage of such methods is that they don’t consider the effect of neighborhood pixels on the final class label. In this letter, a RNN model is proposed for the spectral-spatial classification of hyperspectral image. Specifically, the hyperspectral image cube surrounding a central pixel is considered as a hyperspectral pixels sequence, and a RNN is used to model the dependencies between the different neighborhood pixels. The proposed RNN is conducted on two widely used hyperspectral image datasets. The experimental results demonstrate that the proposed approach provides a better performance than that of conventional methods.

Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis

Hyperspectral image (HSI) has been used for a wide range of applications including forestry, urban planning, and precision agriculture. In recent years, machine learning based algorithms, such as support vector machines, decision trees, ensemble learning, and their variations have shown promising results in HSI analysis. Such methodologies, nevertheless, can lead to insufficient information abstraction in interpreting hyperspectral pixels. In this paper, we propose a novel neural network based classification algorithm, named Progressively Expanded Neural Network (PEN Net), that can effectively interpret hyperspectral pixels in nonlinear feature spaces and then determine their categories. Furthermore, a spectral-spatial HSI classification framework is also introduced to test the generality and robustness of the PEN Net. Experimental results on four standard hyperspectral datasets illustrate that: (1) PEN Net classifier yields better accuracy and competitive processing speed in HSI classification tasks compared to the state-of-the-art methods; (2) Multi-hidden layer based PEN Net generally provides better performance than single hidden layer one; (3) Combination of spectral and spatial features in the PEN Net classifier can significantly improve the classification accuracy by 6–15% compared to the spectral only based HSI classification. This study implies that the proposed neural network architecture opens a new window for future research and the potential for remote sensing image analysis.

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification techniques by taking advantage of both spectral and spatial information in a single classification framework.

Spectral-Spatial Classification of Hyperspectral Images: Three Tricks and a New Learning Setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. When there are only a few labeled pixels for training and a skewed class label distribution, this task becomes very challenging because of the increased risk of overfitting when training a classifier. In this paper, we show that in this setting, a convolutional neural network with a single hidden layer can achieve state-of-the-art performance when three tricks are used: a spectral-locality-aware regularization term and smoothing- and label-based data augmentation. The shallow network architecture prevents overfitting in the presence of many features and few training samples. The locality-aware regularization forces neighboring wavelengths to have similar contributions to the features generated during training. The new data augmentation procedure favors the selection of pixels in smaller classes, which is beneficial for skewed class label distributions. The accuracy of the proposed method is assessed on five publicly available hyperspectral images, where it achieves state-of-the-art results. As other spectral-spatial classification methods, we use the entire image (labeled and unlabeled pixels) to infer the class of its unlabeled pixels. To investigate the positive bias induced by the use of the entire image, we propose a new learning setting where unlabeled pixels are not used for building the classifier. Results show the beneficial effect of the proposed tricks also in this setting and substantiate the advantages of using labeled and unlabeled pixels from the image for hyperspectral image classification.

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the “internal uncertainty” of each independent view, and adopt the inconsistencies between views to estimate the “external uncertainty”. Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

A Bilateral Filter Based Post-Processing Approach for Supervised Spectral-Spatial Hyperspectral Image Classification

A Bilateral Filter Based Post-Processing Approach for Supervised Spectral-Spatial Hyperspectral Image Classification

Sparse Representation-Based Augmented Multinomial Logistic Extreme Learning Machine With Weighted Composite Features for Spectral–Spatial Classification of Hyperspectral Images

Although extreme learning machine (ELM) has been successfully applied to a number of pattern recognition problems, it fails to pro-vide sufficient good results in hyperspectral image (HSI) classification due to two main drawbacks. The first is due to the random weights and bias of ELM, which may lead to ill-posed problems. The second is the lack of spatial information for classification. To tackle these two problems, in this paper, we propose a new framework for ELM based spectral-spatial classification of HSI, where probabilistic modelling with sparse representation and weighted composite features (WCF) are employed respectively to derive the op-timized output weights and extract spatial features. First, the ELM is represented as a concave logarithmic likelihood function under statistical modelling using the maximum a posteriori (MAP). Second, the sparse representation is applied to the Laplacian prior to effi-ciently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm and it has been proven a more efficient method for speed improvement. Third, the spatial information is extracted using the weighted compo-site features (WCFs) to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on two publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and a number of state-of-the-art approaches.

Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

In this paper, a novel spectral-spatial hyperspectral image classification method has been proposed by designing hierarchical subspace switch ensemble learning algorithm. First, the hyperspectral images are processed by fast bilateral filtering to get the spatial features. The spectral features and spatial features are combined to form the initial feature set. Second, Hierarchical instance learning based on iterative means clustering method is designed to obtain hierarchical instance space. Third, random subspace method (RSM) is used for sampling the features and samples, thereby forming multiple sub sample set. After that, semi-supervised learning (S2L) is applied to choose test samples for improving classification performance without touching the class labels. Then, micro noise linear dimension reduction (mNLDR) is used for dimension reduction. Afterwards, ensemble multiple kernels SVM(EMK_SVM) are used for stable classification results. Finally, final classification results are obtained by combining classification results with voting strategy. Experimental results on real hyperspectral scenes demonstrate that the proposed method can effectively improve the classification performance apparently.

Spectral-Spatial Hyperspectral Image Classification based on Randomized Singular Value Decomposition and 3-Dimensional Discrete Wavelet Transform

Spectral-Spatial Hyperspectral Image Classification based on Randomized Singular Value Decomposition and 3-Dimensional Discrete Wavelet Transform

Iterative Target-Constrained Interference-Minimized Classifier for Hyperspectral Classification

Despite the fact that many approaches to hyperspectral image classification are reported, specifically spectral-spatial based methods, this paper presents a rather different approach from a viewpoint of mixed pixel classification, referred to iterative target-constrained interference-minimization classifier (ITCIMC), which includes an iterative Gaussian filtered feedback process to capture the spatial contextual information so as to improve hyperspectral image classification for multiple classes at one-shot operation. In order to evaluate classification performance more effectively, new performance measures other than commonly used overall accuracy (OA) are introduced, particularly, precision rate (PR), misclassification (MC) rate which have been overlooked in hyperspectral image classification. To illustrate the differences among OA, MC rate, and PR, two concepts of a priori classification and a posteriori classification are also proposed from a statistical signal processing point of view. As shown by experiments, ITCMC generally performs significantly better than the existing spectral-spatial hyperspectral image classification techniques in terms of PR and MC rate at the expense of slight loss of OA.

Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction

Hyperspectral image (HSI) classification has been a hot topic of research in recent years. The integration of spectral and spatial context is an effective method for HSI classification. This paper proposes a classification method of HSI based on non-local means (NLM) filtering. Firstly, the classification result of HSI is obtained by adopting the support vector machines. Then, the optimization probability image of spatial structure is obtained by using the spatial context information in the first principal component or the first three principal components of HSI to optimize the initial probability map through the NLM filtering. Finally, the final classification results are calculated based on the maximum probability. Experiment results on three real hyperspectral data demonstrate that the proposed NLM filtering based classification method can improve the classification accuracy significantly. Classification results show the effectiveness and superiority of the proposed methods when compared with other methods.

An unsupervised technique for optimal feature selection in attribute profiles for spectral-spatial classification of hyperspectral images

Inclusion of spatial information along with spectral features play a significant role in classification of remote sensing images. Attribute profiles have already proved their ability to represent spatial information. In order to incorporate proper spatial information, multiple attributes are required and for each attribute large profiles need to be constructed by varying the filter parameter values within a wide range. Thus, the constructed profiles that represent spectral-spatial information of an hyperspectral image have huge dimension which leads to Hughes phenomenon and increases computational burden. To mitigate these problems, this work presents an unsupervised feature selection technique that selects a subset of filtered image from the constructed high dimensional multi-attribute profile which are sufficiently informative to discriminate well among classes. In this regard the proposed technique exploits genetic algorithms (GAs). The fitness function of GAs are defined in an unsupervised way with the help of mutual information. The effectiveness of the proposed technique is assessed using one-against-all support vector machine classifier. The experiments conducted on three hyperspectral data sets show the robustness of the proposed method in terms of computation time and classification accuracy.

Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the last four decades from being a sparse research tool into a commodity product available to a broad user community. Specially, in the last 10 years, a large number of new techniques able to take into account the special properties of hyperspectral data have been introduced for hyperspectral data processing, where hyperspectral image classification, as one of the most active topics, has drawn massive attentions. Spectral-spatial hyperspectral image classification can achieve better classification performance than its pixel-wise counterpart, since the former utilizes not only the information of spectral signature but also that from spatial domain. In this paper, we provide a comprehensive overview on the methods belonging to the category of spectral-spatial classification in a relatively unified context. First, we develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral-spatial methods. Based on such, the categorizations of single-dependency, bilayer-dependency, and multiple-dependency systems are further introduced. Second, we categorize the performings of existing spectral-spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e., preprocessing-based, integrated, postprocessing-based, and hybrid classifications. Then, typical methodologies are outlined. Finally, several representative spectral-spatial classification methods are applied on real-world hyperspectral data in our experiments.

Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter

Explosive growth of applications in hyperspectral image (HSI) has made HSI classification a hot topic in the remote sensing community. The key to improve classification accuracy is how to make full use of the spectral and spatial information. We combine k-nearest neighbor (KNN) algorithm with guided filter which can extract spatial context information and denoise the classification results by edge-preserving filtering. To solve the problem of dimension disaster, we also take dimensionality reduction into account for HSI classification. To verify the feasibility of our proposed methods, we evaluate the performance over four widely used hyperspectral data sets. The experimental results show that with only 5% of samples, our method obtained better performance than improved support vector machine and KNN methods.

Extreme Learning Machine With Enhanced Composite Feature for Spectral-Spatial Hyperspectral Image Classification

The applications of extreme learning machine (ELM) to the hyperspectral-image (HSI) classification have attracted a great deal of research attention because of its excellent performance and fast learning speed. However, conventional ELM is unable to achieve satisfactory accuracy since it only exploits the spectral information to conduct the HSI classification. To address the above issues, we propose a novel classification algorithm based on both spectral and multiscale spatial information, referred to as ELM with enhanced composite feature (ELM-ECF). To be specific, we adopt the original ELM, exploit a multiscale spatial weighted-mean-filtering-based approach to extract multiple spatial information, and use the majority vote method to select the final classification result. The proposed ELM-ECF significantly improves the classification accuracy of the original ELM. Experimental results on three public HSIs (i.e., Indian Pines data set, Pavia University data set, and Salinas data set) illustrate that the proposed ELM-ECF outperforms a variety of the state-of-the-art HSI classification counterparts in terms of classification accuracy.

Spectral-Spatial Hyperspectral Image Classification Based on Mathematical Morphology Post-Processing

Hyperspectral remote sensing sensors can provide plenty of valuable information. Fusion of spectral and spatial information plays a key role in the field of HyperSpectral Image (HSI) classification. In this paper, a novel two stages spectral-spatial HSI classification method based on Mathematical Morphology (MM) post-processing is proposed. In first stage, Support Vector Machine (SVM) is adopted to obtain the initial classification results. In second stage, in order to remove salt and pepper noise, MM is used to refine the obtained results of above stage. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach achieves better accuracy than several recently proposed post-processing HSI classification methods.

An Improved Heuristic Optimization Algorithm for Feature Learning Based on Morphological Filtering and its Application

Hyperspectral remote sensing sensors can provide plenty of valuable information with hundreds of spectral bands at each pixel. Feature selection and spectral-spatial information play an important role in the field of hyperspectral image (HSI) classification. In this paper, a novel two-stage spectral-spatial HSI classification method is proposed. In first stage, the standard particle swarm optimization (PSO) is adopted to optimize the parameters, and a novel binary PSO with mutation mechanism is used for feature selection simultaneously. Then, the support vector machine classifier is performed. In second stage, in order to reduce salt and pepper phenomenon, mathematical morphology post-processing is used to further refine the obtained results of the above stage. Experiments are conducted on two real hyperspectral data sets. The evaluation results show that the proposed approach achieves better accuracy than several state-of-the-art methods.

Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images.

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.

A multiscale modified minimum spanning forest method for spatial-spectral hyperspectral images classification

This paper aimed to present a new method for the spectral-spatial classification of hyperspectral images, based on the idea of modified minimum spanning forest (MMSF). MMSF works on the obtained regions of pre-segmentation step that are considered as nodes of an image graph. In the proposed method, the image is first smoothed by the multiscale edge-preserving filter (MSEPF) and then the MMSF is built in each scale. Finally, all the classification maps of each scale are combined with a majority vote rule. The suggested method, named as MSEPF-MMSF, is performed on four hyperspectral images with different properties, and the experiments deal with the impacts of parameters of filter and the number of markers. The results demonstrate that the proposed method has improved the classification accuracies with respect to the previous methods.