Spectral-spatial Hyperspectral Image Research Articles

High Efficient Deep Feature Extraction and Classification of Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional Neural Networks

Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.

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.

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.

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.

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.

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 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.

A Hybrid Multi-scale Spatial Filtering and Minimum Spanning Forest for Spectral–Spatial Hyperspectral Image Classification

Integration of spatial and spectral information is an effective way in improving classification accuracy. In this article a new framework, based on multi-scale spatial weighted mean filtering (MSWMF) and minimum spanning forest, is proposed for the spectral–spatial classification of hyperspectral images. In the proposed framework, at first the image is smoothed by MSWMF and then the first eight principal components are extracted. Using support vector machine, at each scale of MSWMF, a classification map is produced in order to generate a marker map in the next step. Then, the minimum spanning forest is built on the marker map. Finally, in order to create a final classification map, all the classification maps of each scale are merged with a majority vote rule. The experimental results of the hyper-spectral images indicate that the suggested framework enhances the classification accuracy, in comparison with previously classification techniques. So, it is interesting for hyperspectral images classification.

Discriminative Low-Rank Gabor Filtering for Spectral–Spatial Hyperspectral Image Classification

Spectral–spatial classification of remotely sensed hyperspectral images has attracted a lot of attention in recent years. Although Gabor filtering has been used for feature extraction from hyperspectral images, its capacity to extract relevant information from both the spectral and the spatial domains of the image has not been fully explored yet. In this paper, we present a new discriminative low-rank Gabor filtering (DLRGF) method for spectral–spatial hyperspectral image classification. A main innovation of the proposed approach is that our implementation is accomplished by decomposing the standard 3-D spectral–spatial Gabor filter into eight subfilters, which correspond to different combinations of low-pass and bandpass single-rank filters. Then, we show that only one of the subfilters (i.e., the one that performs low-pass spatial filtering and bandpass spectral filtering) is actually appropriate to extract suitable features based on the characteristics of hyperspectral images. This allows us to perform spectral–spatial classification in a highly discriminative and computationally efficient way, by significantly decreasing the computational complexity (from cubic to linear order) compared with the 3-D spectral–spatial Gabor filter. In order to theoretically prove the discriminative ability of the selected subfilter, we derive an overall classification risk bound to evaluate the discriminating abilities of the features provided by the different subfilters. Our experimental results, conducted using different hyperspectral images, indicate that the proposed DLRGF method exhibits significant improvements in terms of classification accuracy and computational performance when compared with the 3-D spectral–spatial Gabor filter and other state-of-the-art spectral–spatial classification methods.

Class-Specific Random Forest With Cross-Correlation Constraints for Spectral–Spatial Hyperspectral Image Classification

A class-specific random forest (RF) model with cross-correlation constraints is developed for the spectral-spatial hyperspectral image (HSI) classification. The novelties of this letter are as follows: 1) normalization of the spectral feature vector by using cross correlation in the stochastic process and proposal of a spectral-spatial hybrid feature extraction based on the cross-correlation analysis; 2) establishment of an RF classifier model by using class-specific trees (CSTs); and 3) exploration of the performance of the proposed method by comparing with its several traditional classification methods on two real HSI data sets. Further research on the effects of parameter setup, such as the number of CSTs, spectral constraint scale, and size of the spatial neighbor, is discussed in terms of classification accuracy. Experimental results show that the performance of the proposed method is better than that of the traditional methods.

Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification

Hyperspectral image (HSI) classification is one of the fundamental tasks in HSI analysis. Recently, many approaches have been extensively studied to improve the classification performance, among which integrating the spatial information underlying HSIs is a simple yet effective way. However, most of the current approaches haven't fully exploited the spatial information prior. They usually consider this prior either in the step of extracting spatial feature before classification or in the step of post-processing label map after classification, while don't integratively employ the prior in both steps, which thus leaves a room for further enhancing their performance. In this paper, we propose a novel spectral-spatial HSI classification method, which fully utilizes the spatial information in both steps. Firstly, the spatial feature is extracted by applying the 3-dimensional discrete wavelet transform (3D-DWT). Secondly, the local spatial correlation of neighboring pixels is modeled using Markov random field (MRF) based on the probabilistic classification map obtained by applying probabilistic support vector machine (SVM) to the extracted 3D-DWT feature in the first step, and then a maximum a posterior (MAP) classification problem can be formulated in a Bayesian perspective. Finally, α-Expansion min-cut-based optimization algorithm is adopted to solve this MAP problem efficiently. Experimental results on two benchmark HSIs show that the proposed method achieves a significant performance gain beyond state-of-the-art methods.

Local histogram and discriminative learning-based hyperspectral data classification

ABSTRACTA spectral–spatial hyperspectral image classification method is proposed in this article. The proposed method is local histogram and discriminative based classification (LHD). It is implemented in two steps. In the first step, a projection matrix is obtained by maximizing the class discrimination and preserving the local structure of data using both the spectral and spatial neighbours. In the second step, the contextual features are extracted from the local regions using the histogram as a nonparametric statistical estimate. Finally, with incorporating spectral and spatial features, the classification map is obtained. The experimental results demonstrate that the proposed method is superior to some state-of-the-art alternatives.

Spectral–spatial hyperspectral image ensemble classification via joint sparse representation

Ensemble learning can improve the performance of classification by integrating a set of classifiers, and shows significant potential benefits to the classification of hyperspectral image. However, the ensemble strategy remarkably influences the classification results, which include determining the minimum number of classifiers and assigning advisable weights associated with each classifier. In this paper, we present a novel sparse ensemble learning method with spectral–spatial knowledge for hyperspectral image classification. It considers the ensemble strategy under sparse recovery framework, where the solved non-zero coefficients reveal the importance of the selected classifier, from which a compact and effective ensemble learning system can be derived. Moreover, the spatial information is incorporated into the classification to develop a spectral–spatial joint sparse representation based ensemble learning algorithm for more accurate classification of hyperspectral images. Experimental results on several real hyperspectral images show that the proposed sparse ensemble system can achieve better performance than traditional ensemble learning methods using all classifiers, and it largely improves the efficiency in testing phase.

A Discontinuity Preserving Relaxation Scheme for Spectral–Spatial Hyperspectral Image Classification

In remote sensing image processing, relaxation is defined as a method that uses the local relationship among neighboring pixels to correct spectral or spatial distortions. In recent years, relaxation methods have shown great success in classification of remotely sensed data. Relaxation, as a preprocessing step, can reduce noise and improve the class separability in the spectral domain. On the other hand, relaxation (as a postprocessing approach) works on the label image or class probabilities obtained from pixelwise classifiers. In this work, we develop a discontinuity preserving relaxation strategy, which can be used for postprocessing of class probability estimates, as well as preprocessing of the original hyperspectral image. The newly proposed method is an iterative relaxation procedure, which exploits spatial information in such a way that it considers discontinuities existing in the data cube. Our experimental results indicate that the proposed methodology leads to state-of-the-art classification results when combined with probabilistic classifiers for several widely used hyperspectral data sets, even when very limited training samples are available.

Spectral–Spatial Hyperspectral Image Classification Based on KNN

Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification. In this paper, a novel spectral–spatial hyperspectral image classification method based on K nearest neighbor (KNN) is proposed, which consists of the following steps. First, the support vector machine is adopted to obtain the initial classification probability maps which reflect the probability that each hyperspectral pixel belongs to different classes. Then, the obtained pixel-wise probability maps are refined with the proposed KNN filtering algorithm that is based on matching and averaging nonlocal neighborhoods. The proposed method does not need sophisticated segmentation and optimization strategies while still being able to make full use of the nonlocal principle of real images by using KNN, and thus, providing competitive classification with fast computation. Experiments performed on two real hyperspectral data sets show that the classification results obtained by the proposed method are comparable to several recently proposed hyperspectral image classification methods.

Spectral–spatial hyperspectral image classification with adaptive mean filter and jump regression detection

A new remotely sensed hyperspectral image data classification algorithm which integrates the adaptive mean filter and jump regression in a variational framework is introduced. First, the adaptive mean filter is used to build the posterior probability distributions in each subpixel, and the jump detection method is then used to provide the content-aware information to adjust the smoothing extent of total variation in image discontinuous areas. Experimental results on real hyperspectral datasets show the relatively good performance of the proposed algorithm in terms of the overall accuracy, average accuracy and kappa statistic.

Spectral–Spatial Hyperspectral Image Classification Using $\ell_{1/2}$ Regularized Low-Rank Representation and Sparse Representation-Based Graph Cuts

Hundreds of narrow contiguous spectral bands collected by a hyperspectral sensor have provided the opportunity to identify the various materials present on the surface. Moreover, spatial information, enforcing the assumption that the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, two predominant approaches have been developed to exploit the spatial information. First, by decomposing each pixel and the spatial neighborhood into a low-rank form, the spatial information can be efficiently integrated into the spectral signatures. Meanwhile, in order to describe the low-rank structure of the decomposed data more precisely, an $\ell_{1/2}$ norm regularization is introduced and a discrete algorithm is proposed to solve the combined optimization problem by the augmented Lagrange multiplier (ALM) and a half-threshold operator. Second, a graph cuts segmentation algorithm has been applied on the sparse-representation-based probability estimates of the hyperspectral data to further improve the spatial homogeneity of the material distribution. Experimental results on four real hyperspectral data with different spectral and spatial resolutions have demonstrated the effectiveness and versatility of the proposed spatial information-fused approaches for hyperspectral image classification.

Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields

This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic.

Joint sparse hyperspectral image classification based on adaptive spatial context

Hyperspectral image (HSI) analysis is attracting a growing interest in real-world applications, many of which can finally be transformed into classification tasks. Traditional spectral-spatial HSI classification methods take advantage of the identical spatial information that is available everywhere, but this is not always the case, especially in the class boundary. A method for HSI classification based on the spectral information and the adaptive spatial context is proposed. First, we introduce a high-dimensional steering kernel to describe the adaptive spatial context and select the spatial correlative pixels of a given test pixel according to the adaptive spatial context. The selected pixels can be simultaneously sparse represented by linear combinations of a few common training samples. Then, a classifier imposing the adaptive spatial context to determine the final label of the test pixel is proposed. Experimental results on real HSIs show that our algorithm outperforms other state-of-art algorithms.