Extended Multi-attribute Profiles Research Articles

Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) plays an important role in military and civilian applications and has attracted a lot of research. The well-known Reed–Xiaoli (RX) algorithm is the benchmark of HAD methods. Based on the RX model, many variants have been developed. However, most of them ignore the spatial characteristics of hyperspectral images (HSIs). In this paper, we combine the extended multi-attribute profiles (EMAP) and RX algorithm to propose the Recursive RX with Extended Multi-Attribute Profiles (RRXEMAP) algorithm. Firstly, EMAP is utilized to extract the spatial structure information of HSI. Then, a simple method of background purification is proposed. That is, the background is purified by utilizing the RX detector to remove the pixels that are more likely to be anomalies, which helps improve the ability of background estimation. In addition, a parameter is utilized to control the purification level and can be selected by experiments. Finally, the RX detector is used again between the EMAP feature and the new background distribution to judge the anomaly. Experimental results on six real hyperspectral datasets and a synthetic dataset demonstrate the effectiveness of the proposed RRXEMAP method and the importance of using the EMAP feature and background purity means. Especially, on the abu-airport-2 dataset, the AUC value obtained by the present method is 0.9858, which is higher than the second one, CRD, by 0.0198.

Ensemble and Random RX With Multiple Features Anomaly Detector for Hyperspectral Image

Hyperspectral anomaly detection (HAD) has always been a hot topic in hyperspectral image (HSI) processing. The Reed-Xiaoli (RX) detector is one of the most widely used methods for HAD. However, most improved versions of RX are usually only improved from the view of reducing the contamination of anomaly pixels on the background modeling. Considering the rich spatial information of HSI and the influence of anomaly pixels on background modeling, we propose an ensemble and random RX with multiple features (ERRX MFs) anomaly detector. First, the spectral, Gabor, extended morphological profile (EMP), and extended multiattribute profile (EMAP) four features are extracted. Then, random subsampling is used on RX to obtain multiple detection results for each feature. Finally, the detection results of these four features are further fused to the final detection result by ensemble learning. Experimental results on four real hyperspectral datasets validate that our proposed method can achieve more satisfactory results compared with other state-of-the-art methods.

Superpixel-Based Relaxed Collaborative Representation With Band Weighting for Hyperspectral Image Classification

Representation learning methods, such as sparse representation (SR) and collaborative representation (CR), have been widely used in hyperspectral image classification. However, they merely considered the similarities between features. Due to the plentiful spatial and spectral information in hyperspectral images, the differences between features also need to be considered. Relaxed CR (RCR) is used in face recognition to accommodate the difference and similarity of features simultaneously. In this article, a novel method of RCR with band weighting based on superpixel segmentation is proposed for hyperspectral image classification. The <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {l}_{ \boldsymbol {2}}$ </tex-math></inline-formula> norm on band coefficients and global average coefficients is exploited to ensure the similarity, and the variance determines the specific coefficient-related weight of each band. The training set is selected from each superpixel, which is considered as a subgraph rather than independent pixels. It is favorable for concentrating on the difference between similar bands since the samples in each superpixel are of high similarity. Furthermore, extended multiattribute profile (EMAP) features, Gabor features, and local binary pattern (LBP) features are employed to increase the diversity of features; thus, a method of multifeatures&#x2019; RCR based on superpixels is proposed. Three typical data are used to validate the related algorithms. The experiments demonstrate that the proposed algorithms can effectively improve classification accuracy compared to state-of-the-art classifiers.

Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with Stacked Autoencoder

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

A Multiview Spectral–Spatial Feature Extraction and Fusion Framework for Hyperspectral Image Classification

Hyperspectral images (HSIs) can provide abundant and diverse features which are helpful for classification, such as spectral, texture, and shape features. Combining these features can enhance the ability to describe the characteristics of different classes of land covers. However, most of the existing methods usually stack features from multiple views to construct high-dimensional data and then learn information from it which may waste information inherent in different feature spaces. In this letter, we proposed a multiview spectral–spatial feature extraction (FE) and fusion framework for the analysis and classification of the HSIs. First, different and complementary spatial features extracted by extended multiattribute profiles (EMAPs), gray-level cooccurrence matrix (GLCM), and Gabor from the original HSI are, respectively, stacked with the spectral bands to construct multiview data set for one single scene of HSI. Thus, each sample can be represented in different spectral–spatial domains. Then, a semisupervised FE method, which combines local fisher discriminant analysis (LFDA) that explores discriminative information from limited labeled samples and the improved neighborhood preserving embedding (NPE) that aims at maintaining the local neighborhood structure from a global perspective, is applied on the multiview data set to eliminate redundant information and obtain multiview spectral–spatial features. Note that the improved NPE which adds spatial interpixel correlations to similarity measure between samples is applied on all the samples rather than unlabeled samples, and hence, the best spatial nearby neighbors for each sample could be found from the whole data. Finally, we integrate these multiview features with diversity and complementarity to construct the intact feature representation for each sample. The experimental results show that the proposed method outperforms the state-of-the-art multiview FE methods for HSI classification.

Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

Multiscale Adjacent Superpixel-Based Extended Multi-Attribute Profiles Embedded Multiple Kernel Learning Method for Hyperspectral Classification

In this paper, superpixel features and extended multi-attribute profiles (EMAPs) are embedded in a multiple kernel learning framework to simultaneously exploit the local and multiscale information in both spatial and spectral dimensions for hyperspectral image (HSI) classification. First, the original HSI is reduced to three principal components in the spectral domain using principal component analysis (PCA). Then, a fast and efficient segmentation algorithm named simple linear iterative clustering is utilized to segment the principal components into a certain number of superpixels. By setting different numbers of superpixels, a set of multiscale homogenous regional features is extracted. Based on those extracted superpixels and their first-order adjacent superpixels, EMAPs with multimodal features are extracted and embedded into the multiple kernel framework to generate different spatial and spectral kernels. Finally, a PCA-based kernel learning algorithm is used to learn an optimal kernel that contains multiscale and multimodal information. The experimental results on two well-known datasets validate the effectiveness and efficiency of the proposed method compared with several state-of-the-art HSI classifiers.

Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.

Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery

Hyperspectral anomaly detection (HAD) has drawn a significant attention of late due to its importance in many military and civilian applications. In this article, a fast hyperspectral anomaly detector that combines multiple features and isolation forest is proposed. This approach, which is based on the assumption that the anomalous pixels are more susceptible to isolation than the background pixels, consists of two main parts. First, the spectral, Gabor, extended morphological profile (EMP) and extended multiattribute profile (EMAP) features are extracted from the hyperspectral image (HSI). Next, the isolation forest of each feature is constructed using the subsampling strategy. This combination of multiple features can exploit both the spectral and spatial information of the HSI, thereby improving the anomaly detection performance significantly. Compared with eight state-of-the-art HAD methods, the experimental results on four real hyperspectral data sets demonstrate that the performance of our proposed approach is quite competitive in terms of detection accuracy and running time.

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.

Deep Learning for Land Cover Classification Using Only a Few Bands

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

Improving Land Cover Classification Using Extended Multi-Attribute Profiles (EMAP) Enhanced Color, Near Infrared, and LiDAR Data

Hyperspectral (HS) data have found a wide range of applications in recent years. Researchers observed that more spectral information helps land cover classification performance in many cases. However, in some practical applications, HS data may not be available, due to cost, data storage, or bandwidth issues. Instead, users may only have RGB and near infrared (NIR) bands available for land cover classification. Sometimes, light detection and ranging (LiDAR) data may also be available to assist land cover classification. A natural research problem is to investigate how well land cover classification can be achieved under the aforementioned data constraints. In this paper, we investigate the performance of land cover classification while only using four bands (RGB+NIR) or five bands (RGB+NIR+LiDAR). A number of algorithms have been applied to a well-known dataset (2013 IEEE Geoscience and Remote Sensing Society Data Fusion Contest). One key observation is that some algorithms can achieve better land cover classification performance by using only four bands as compared to that of using all 144 bands in the original hyperspectral data with the help of synthetic bands generated by Extended Multi-attribute Profiles (EMAP). Moreover, LiDAR data do improve the land cover classification performance even further.

Integrating Spectral and Spatial features for Hyperspectral Image Classification with a Modified Composite Kernel Framework

This paper presents two modified composite kernel frameworks for the hyperspectral image classification. The first proposed framework is a simple extension of the previously proposed generalized composite kernel (GCK) approach, and in the second framework, we have used a voting strategy. The developed modified composite kernels combine different spatial and spectral profiles without considering any weight parameter. We have used the multinomial logistic regression (MLR) algorithm for the classification of hyperspectral images in this work. We have developed different spatial profiles: extended morphological profile (EMP) and extended multiattribute profile (EMAP) in this work, and further integrated them with the spectral profile for spectral-spatial classification. Experimental results with the AVIRIS Indian Pines, ROSIS Pavia university area and AVIRIS Salinas images indicate that the proposed framework leads to an improvement in classification accuracy. The obtained overall accuracy increases by 2–3% for AVIRIS Indian Pines scene, 1–2% for ROSIS Pavia University Area scene and 1–3% for AVIRIS Salinas scene with the first proposed framework. In case of the second proposed framework with a sufficient number of training samples per class, the overall accuracy increases by 3–7% for AVIRIS Indian Pines scene, 2.5% for ROSIS Pavia University Area scene and 7–8% for AVIRIS Salinas scene. For this framework (Modified-CKV with MLR) we have obtained the overall accuracy of 96.95% for the Indian Pines scene, 99.33% for the Pavia University Area, and 97.76% for the Salinas scene with 75 training samples per class, which is less than 2% of the available labelled samples. With the results obtained, we have also concluded that with the linear combination approach for the construction of composite kernels, the overall accuracy improves without any increase in computational complexity.

3-D Gaussian–Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification

Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian–Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian–Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian–Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones.

Performance of Change Detection Algorithms Using Heterogeneous Images and Extended Multi-attribute Profiles (EMAPs)

We present detection performance of ten change detection algorithms with and without the use of Extended Multi-Attribute Profiles (EMAPs). Heterogeneous image pairs (also known as multimodal image pairs), which are acquired by different imagers, are used as the pre-event and post-event images in the investigations. The objective of this work is to examine if the use of EMAP, which generates synthetic bands, can improve the detection performances of these change detection algorithms. Extensive experiments using five heterogeneous image pairs and ten change detection algorithms were carried out. It was observed that in 34 out of 50 cases, change detection performance was improved with EMAP. A consistent detection performance boost in all five datasets was observed with EMAP for Homogeneous Pixel Transformation (HPT), Chronochrome (CC), and Covariance Equalization (CE) change detection algorithms.

Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification

In virtue of the spatial structural characteristic of surface materials, the performance of the hyperspectral image classification can be boosted by incorporating texture information. Normally, the spatial structure can be extracted by predefined operators, including the popular extended multiattribute profiles (EMAPs) and the Gabor filters. Recently, superpixel segmentation, which reflects the homogeneous regularity of objects, has drawn much attention in the field. In this paper, a collaborative representation-based multiscale superpixel fusion (CRMSF) approach has been proposed for the hyperspectral image classification. First, after obtaining the EMAPs from the raw hyperspectral image, a group of predesigned 3-D Gabor wavelet filters is convolved with the EMAP features, and the EMAP-Gabor features can, thus, be achieved. Second, the collaborative representation-based classification (CRC) is employed to fully and efficiently make use of the huge amount of extracted EMAP-Gabor features. Third, multiscale superpixel maps are generated from the EMAP features that are utilized to regularize the classification map obtained by CRC. A heuristic strategy has been specially devised to automatically decide the number of extracted superpixels in multiple scales, which can be perfectly compatible with hyperspectral images having various spatial sizes and spatial resolutions. This is the most important contribution of the developed CRMSF approach. Finally, the classification task is accomplished by fusing the multiple regularized classification maps. The CRMSF approach has been evaluated on four popular hyperspectral image data sets, and the experimental results show the advantages of CRMSF, particularly for a hyperspectral image with high spatial resolution.

Dual-Stage Construction of Probability for Hyperspectral Image Classification

Recently, feature extraction-based methods have received increasing attention in the hyperspectral image. In this letter, to ensure a more powerful discriminative ability of extracted features, a dual-stage construction of probability (DSCP) method is proposed for hyperspectral image classification. Specifically, the extended multi-attribute profiles (EMAP) method is applied to extract the shape feature of hyperspectral remote sensing image (HSI) to obtain a more accurate initial probability map. Considering that there are still some noises in the boundaries of the initial probability map, an effective edge-preserving filter-based approach named rolling guidance filter is used for probability post-optimization. Consequently, the class label of each pixel can be determined according to the optimized probability maps. Experiments demonstrate significantly the efficiency of the proposed method in comparison with other advanced methods.

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection

Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.

Deep Multi-Feature Learning for Water Body Extraction from Landsat Imagery

Water body extraction from remote sensing image data has been becoming a really hot topic. Recently, researchers put forward numerous methods for water body extraction, while most of them rely on elaborative feature selection and enough number of training samples. Convolution Neural Network (CNN), one of the implementation models of deep learning, has strong capability for two-dimension images’ classification. A new water body extraction model based on CNNs is established for deep multi-feature learning. Before experiment, image enhancement will be done by Dark Channel Prior. Then we concatenate three kinds of features: spectral information, spatial information that is extracted by Extended Multi-attribute Profile (EMAP) and various water indexes firstly. Next, feature matrixes are acted as the input of CNN-based model for training and classifying. The experimental results showed that the proposed model has better classification performance than Support Vector Machine (SVM) and artificial neural network (ANN). On very limited training set, our model could learn unique and representative features for better water body extraction.