Spectral-spatial Classification Research Articles

Spatial–Spectral Image Classification with Edge Preserving Method

Classification of remotely sensed imagery with the integration of spatial context should be a constructive way of improved accuracy in image classification. An efficient spatial–spectral classification approach with colorimetric edge preservation of spatial–spectral modeling is processed. This research paper consists of three phases first, and the multispectral imagery [Captured by unmanned aerial vehicles (UAV)] is classified using a per-pixel classification method, i.e., support vector machine (SVM). Then, the classified image is constituted as various probability thematic maps and an edge preservation using chromaticity mapping is conducted on each probability map, with the principal component analysis of the multispectral imagery (Captured by UAVs) using as the color or gray-guidance imagery. Finally, from the edge preservation probability images, the classes of all the pixels are specified with the maximum probabilities. Significantly, the accuracy assessment of image classification improved in a short computational time.

Union of Class-Dependent Collaborative Representation Based on Maximum Margin Projection for Hyperspectral Imagery Classification

This article proposed a novel spectral-spatial classification framework for hyperspectral image (HSI) through combining collaborative representation (CR) and maximum margin projection (MMP). First, class-dependent CR classifier (CDCRC) is used on HSI classification to fully make use of self-information contained in each class. Second, the MMP is included into the framework to discover local manifold structure. Combined with CDCRC, it formed the classifier named CDCRC based on MMP (CMCRC), which aims to reduce band redundancy. Finally, a comprehensive spectral-spatial classifier, called union of CMCRC, is proposed to optimize the classification map through integrating cumulative probability of residuals instead of applying strong constraints to maintain the spatial consistency. Experimental results on three real hyperspectral datasets demonstrate the effectiveness and practicality of the proposed methods over other related models for HSI classification tasks.

Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.

Ensemble of ERDTs for Spectral-Spatial Classification of Hyperspectral Images Using MRS Object-Guided Morphological Profiles.

In spectral-spatial classification of hyperspectral image tasks, the performance of conventional morphological profiles (MPs) that use a sequence of structural elements (SEs) with predefined sizes and shapes could be limited by mismatching all the sizes and shapes of real-world objects in an image. To overcome such limitation, this paper proposes the use of object-guided morphological profiles (OMPs) by adopting multiresolution segmentation (MRS)-based objects as SEs for morphological closing and opening by geodesic reconstruction. Additionally, the ExtraTrees, bagging, adaptive boosting (AdaBoost), and MultiBoost ensemble versions of the extremely randomized decision trees (ERDTs) are introduced and comparatively investigated for spectral-spatial classification of hyperspectral images. Two hyperspectral benchmark images are used to validate the proposed approaches in terms of classification accuracy. The experimental results confirm the effectiveness of the proposed spatial feature extractors and ensemble classifiers.

Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods.

Extraction of tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu via spectral-spatial classification using UAV-based hyperspectral images

BackgroundTree crown extraction is an important research topic in forest resource monitoring. In particular, it is a prerequisite for disease detection and mapping the degree of damage caused by forest pests. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is effective for surveying and monitoring forest health. This article proposes a spectral-spatial classification framework that uses UAV-based hyperspectral images and combines a support vector machine (SVM) with an edge-preserving filter (EPF) for completing classification more finely to automatically extract tree crowns damaged by Dendrolimus tabulaeformis Tsai et Liu (D. tabulaeformis) in Jianping county of Liaoning province, China.ResultsExperiments were conducted using UAV-based hyperspectral images, and the accuracy of the results was assessed using the mean structure similarity index (MSSIM), the overall accuracy (OA), kappa coefficient, and classification accuracy of damaged Pinus tabulaeformis. Optimized results showed that the OA of the spectral-spatial classification method can reach 93.17%, and the extraction accuracy of damaged tree crowns is 7.50–9.74% higher than that achieved using the traditional SVM classifier.ConclusionThis study is one of only a few in which a UAV-based hyperspectral image has been used to extract tree crowns damaged by D. tabulaeformis. Moreover, the proposed classification method can effectively extract damaged tree crowns; hence, it can serve as a reference for future studies on both forest health monitoring and larger-scale forest pest and disease assessment.

Fusion of Spectral–Spatial Classifiers for Hyperspectral Image Classification

A spectral-spatial (SS) hyperspectral classifier generally implements a spectral classifier (SC) followed by a spatial filter (SF) for classification. This article develops a new approach to fusing multiple SC-SF classifiers for hyperspectral image classification (HSIC) as to improve classification performance. To accomplish this goal an iterative process is particularly designed to fuse the spatial-filtered classification maps (SFMaps) produced by each of SC-SF classifiers into one single SFMap via maximum a posteriori (MAP) criterion. Such fused SFMaps are then fed back and added to the current data cube to create a new data set for next round SC-SF classifier fusion. The same process is repeated iteratively until it satisfies an automatic stopping rule. To further fuse more than two SS methods, two approaches are also developed, called simultaneous multiple SC-SF fusion (SMSSF) method and progressive multiple SC-SF fusion (PMSSF) method. Experimental results demonstrate that fusing multiple SC-SF classifiers can indeed perform better than using an individual single SC-SF classifier alone without fusion.

EMAP-DCNN: A NOVEL MATHEMATICAL MORPHOLOGY AND DEEP LEARNING COMBINED FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. The classification of hyperspectral image (HSI) with high spectral and spatial resolution represents an important and challenging task in image processing and remote sensing (RS) domains due to the problem of computational complexity and big dimensionality of the remote sensing images. The spatial and spectral pixel characteristics have crucial significance for hyperspectral image classification and to take into account these two types of characteristics, various classification and feature extraction methods have been developed to improve spectral-spatial classification of remote sensing images for thematic mapping purposes such as agricultural mapping, urban mapping, emergency mapping in case of natural disasters... In recent years, mathematical morphology and deep learning (DL) have been recognized as prominent feature extraction techniques that led to remarkable spectral-spatial classification performances. Among them, Extended Multi-Attribute Profiles (EMAP) and Dense Convolutional Neural Network (DCNN) are considered as robust and powerful approaches such as the work in this paper is based on these two techniques for the feature extraction stage and used in two combined manners and constructing the EMAP-DCNN frame. The experiments were conducted on two popular datasets: “Indian Pines” and “Huston” hyperspectral datasets. Experimental results demonstrate that the two proposed approaches of the EMAP-DCNN frame denoted EMAP-DCNN 1, EMAP-DCNN 2 provide competitive performances compared with some state-of-the-art spectral-spatial classification methods based on deep learning.

Spectral-spatial classification method for hyperspectral images using stacked sparse autoencoder suitable in limited labelled samples situation

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods.

RMCNet: Random Multiscale Convolutional Network for Hyperspectral Image Classification

To address the limitation of the high-dimensionality features and single spatial scale in the spectral–spatial classification of hyperspectral image (HSI), we propose a random multiscale convolutional network (RMCNet) that combines a multiscale dimensionality reduction module (MDRM) and the RMCNet for improving classification accuracy. The MDRM is based on multiscale superpixel segmentations, which implements dimensionality reduction leading to relieve the Hughes problem and reduce the computation burden in deep learning. Then, the multiscale spectral–spatial features are extracted by the RMCNet to adaptive various complex scenes in HSI. Finally, the multiscale spectral–spatial features act as inputs of support vector machine for classification. In the experiments, three benchmark HSIs are used to evaluate the performance of the proposed method. The experimental results demonstrate that the RMCNet can yield a competitive performance compared with the state-of-the-art methods.

Attribute Profiles of Different Attributes for Spectral-Spatial Classification of Hyperspectral Imagery

Incorporation of spatial information along with spectral features has a great impact on the analysis of hyperspectral images (HSIs). Attribute profiles have been proved to be a very effective state-of-the-art method for fusing spectral and spatial information in HSI. This paper recalls the construction of attribute profiles (APs) for HSI classification and provides a comprehensive analysis of different properties of connected components that can be used as an attribute while constructing APs. Various attributes have been widely and successfully used to construct attribute profiles for spectral-spatial classification of HSIs. In this paper, we investigate several unexplored attributes in addition to the existing ones. An empirical study on the attribute profiles, constructed by using different attributes, is carried out considering three real HSI data sets. Our study reveals that each attribute possesses interesting filtering capabilities among which the attributes namely complexity and diameter of equivalent circle, which are new in the HSI literature, are found most promising to incorporate spatial information for HSI classification. The paper also lists out suitable attributes for recognition of different classes in a remote sensing scene.

Hyperspectral Image Classification With Attention-Aided CNNs

Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image and then fed into CNNs to extract spectral and spatial features. It is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. If fully explored, this prior information will help improve the learning capacity of CNNs. Along this direction, we propose an attention aided CNN model for spectral-spatial classification of hyperspectral images. Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively. Both of them are based on the traditional CNN model, and incorporate attention modules to aid networks focus on more discriminative channels or positions. In the final classification phase, the spectral classification result and the spatial classification result are combined together via an adaptively weighted summation method. To evaluate the effectiveness of the proposed model, we conduct experiments on three standard hyperspectral datasets. The experimental results show that the proposed model can achieve superior performance compared to several state-of-the-art CNN-related models.

Weighted Sparse Graph Regularization for Spectral–Spatial Classification of Hyperspectral Images

In this letter, we propose a novel weighted sparse graph regularization (SGR) (WSGR) model by incorporating the spatial information both in pre and postprocessing stages. In the preprocessing stage, we generate adaptive patch features to represent the spatial information. Traditional patch feature extraction methods rarely consider the quality of the neighboring pixels within a patch since the central pixel and its neighbors may belong to different classes. To solve this issue, a Gaussian-kernel-based weighting scheme that can adaptively model the neighborhood information is introduced into the original SGR model. Experimental results, conducted with two popular hyperspectral data sets, indicate that the proposed method outperforms other related methods with an overall accuracy higher than 90% when only 20 labeled samples per class are used for training.

Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives.

Domain Transform Filter and Spatial-Aware Collaborative Representation for Hyperspectral Image Classification Using Few Labeled Samples

Spectral-spatial classification methods based on filtering and collaborative representation (CR) have an upward trend in the classification of hyperspectral images. However, some of them may give poor performances when the number of training samples is limited. To overcome this problem, a classification method (DF+SACR) that includes domain transform filter (DF) and spatial-aware CR (SACR) is proposed in this letter. The proposed method first reduces the dimensionality. Next, DF that is an edge-aware smoothing method is performed on the reduced data set. In the final step, the output of DF is classified using an SACR method. Alternatively, a faster version of DF+SACR that utilizes superpixels instead of pixels is proposed as a second method in this letter. Experiments performed on the Indian Pines, Pavia University, University of Houston, and Salinas data sets demonstrate that the proposed methods not only improve the performance of the SACR method but also achieve higher classification accuracies and lower computation times than state-of-the-art methods at limited training samples.

Composite Clustering Sampling Strategy for Multiscale Spectral-Spatial Classification of Hyperspectral Images

In recent years, many high-performance spectral-spatial classification methods were proposed in the field of hyperspectral image classification. At present, a great quantity of studies has focused on developing methods to improve classification accuracy. However, some research has shown that the widely adopted pixel-based random sampling strategy is not suitable for spectral-spatial hyperspectral image classification algorithms. Therefore, a composite clustering sampling strategy is proposed, which can greatly reduce the overlap between the training set and the test set, while making sample points in the training set sufficiently representative in the spectral domain. At the same time, in order to solve problems of a three-dimensional Convolutional Neural Network which is commonly used in spectral-spatial hyperspectral image classification methods, such as long training time and large computing resource requirements, a multiscale spectral-spatial hyperspectral image classification model based on a two-dimensional Convolutional Neural Network is proposed, which effectively reduces the training time and computing resource requirements.

Spectral-spatial hyperspectral image classification based on superpixel and multi-classifier fusion

ABSTRACT Hyperspectral image classification is a challenging problem for machine learning methods due to the small number of labelled samples and high spectral variability. In this paper, to solve this problem, a novel superpixel and multi-classifier fusion (SMCF)-based classification method for hyperspectral images is proposed. This method takes full advantage of the spectral information of superpixels and the spatial information of hyperspectral images and includes the following three steps. First, superpixels are used to increase the number of training samples and their spectral diversity. Second, label propagation (LP) is used to classify the hyperspectral images. Although LP is an efficient semi-supervised classification method, the corresponding performance is poor for certain land cover types with dispersed spatial distributions. Thus, a support vector machine (SVM) classifier is introduced to classify the hyperspectral images. Finally, the results of the SVM and LP classifiers are combined using our new class-specific weighted fusion algorithm. In the experiments, we selected three widely used and real hyperspectral data sets for evaluation. The final classification performance was evaluated based on two common metrics: the overall accuracy (OA) and the Kappa coefficient. The experimental results show that the proposed SMCF method is superior to six well-known classification methods, even when only 1% or less of the labelled samples are used.

Spectral–Spatial Hyperspectral Classification via Structural-Kernel Collaborative Representation

This letter introduces a novel spatial-spectral classification method for hyperspectral images (HSIs) based on a structural-kernel collaborative representation (SKCR), which considers one weak assumption of spatial neighborhood that of the pixels in a superpixel belong to the same class when exploiting contextual information in HSI. The proposed method consists of the following steps. First, a superpixel segmentation strategy is used to construct self-adaptive regions for the HSI. Then, the structural information within each superpixel block is extracted based on the density peak and K nearest neighbors. Next, dual kernels are separately utilized for the exploitation of the spectral and the spatial information. Finally, the dual kernels are combined and incorporated into a support-vector-machine classifier. Since the weak assumption of spatial neighborhood is well considered in the collaborative representation, the proposed method showed excellent classification performance for two widely used real hyperspectral data sets even when the number of training samples was relatively small.

Multiscale dual-level network for hyperspectral image classification

We propose a new dual-level convolutional neural network model based on Inception modules and residual connections. First, Inception has filters with different kernel sizes, and its output feature maps contain different scales of receptive fields. The feature map with the wide receptive field receives the global information, while the feature map with the small receptive field contains some local information. The multiscale feature provides more comprehensive information. Second, with the help of residual connections, the training process is simple and can avoid overfitting. Third, the proposed network adopts two levels, i.e., a low level and a high level, and uses the feature fusion operation to take full advantage of the complementary and correlated information of the two levels. Fourth, we combine the spatial features and the spectral features of hyperspectral image (HSI). The pixels to be classified with their neighborhood information serve as the input of the neural network to realize spectral–spatial classification for HSI. Experimental results show that our model performs better than other state-of-the-art methods.

Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification

Over the past few years making use of deep networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), classifying hyperspectral images has progressed significantly and gained increasing attention. In spite of being successful, these networks need an adequate supply of labeled training instances for supervised learning, which, however, is quite costly to collect. On the other hand, unlabeled data can be accessed in almost arbitrary amounts. Hence it would be conceptually of great interest to explore networks that are able to exploit labeled and unlabeled data simultaneously for hyperspectral image classification. In this article, we propose a novel graph-based semisupervised network called nonlocal graph convolutional network (nonlocal GCN). Unlike existing CNNs and RNNs that receive pixels or patches of a hyperspectral image as inputs, this network takes the whole image (including both labeled and unlabeled data) in. More specifically, a nonlocal graph is first calculated. Given this graph representation, a couple of graph convolutional layers are used to extract features. Finally, the semisupervised learning of the network is done by using a cross-entropy error over all labeled instances. Note that the nonlocal GCN is end-to-end trainable. We demonstrate in extensive experiments that compared with state-of-the-art spectral classifiers and spectral-spatial classification networks, the nonlocal GCN is able to offer competitive results and high-quality classification maps (with fine boundaries and without noisy scattered points of misclassification).