Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging

The conventional method used in forest inventory is time-consuming, especially in field surveying and requires an expert’s knowledge and observation to do species identification. This standard method can be facilitated by advances in remote sensing technology. An airborne hyperspectral image mosaic was utilised in the research to evaluate the ability of this high spectral fidelity dataset that was incorporated into a feature extraction Support Vector Machine (SVM) classifier to successfully detect and map a tree species of interest (Shorea kunstleri King) within the mixed dipterocarp forest stand of the Lambir Hills National Park. In this study, two types of feature extraction Principal Component Analysis (PCA) and Spectral Derivative (SD) were applied to classifiers SVM. To demonstrate the effect, classification of the hyperspectral image with no feature extraction was done. The accuracies achieved from different feature extraction were compared statistically. Based on the results, Shorea kunstleri King was able to be discriminated against within 53 species of Shorea. SVM classifier is known for its ability to generalise well even with limited training samples and is commonly used in image classification. The Kernel parameter in SVM classifier alongside feature extraction significantly affects the classification accuracy. Therefore, feature extraction does affect the classification accuracy, but it depends on the morphology of the study area. It is concluded that the SVM method with SD feature extraction could detect tree crowns of Shorea kunstleri with a Kappa coefficient of 0.8126 which showed a great agreement with the observed samples and overall accuracy of 89.89% that shows the accuracy of the final map.

PurposeCurrent practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.Design/methodology/approachThree novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.FindingsThe proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.Originality/valueTo the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

ABSTRACTHyperspectral image classification is among the most frequent topics of research in recent publications. This paper proposes a new supervised linear feature extraction method for classification of hyperspectral images using orthogonal linear discriminant analysis in both spatial and spectral domains. In fact, an orthogonal filter set and a spectral data transformation are designed simultaneously by maximizing the class separability. The important characteristic of the presented approach is that the proposed filter set is supervised and considers the class separability when extracting the features, thus it is more appropriate for feature extraction compared with other filters such as Gabor. In order to compare the proposed method with some existing methods, the extracted spatial–spectral features are fed into a support vector machine classifier. Some experiments on the widely used hyperspectral images, namely Indian Pines, Pavia University, and Salinas data sets, reveal that the proposed approach leads to state-of-the-art performance when compared to other recent approaches.

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

The problem with development of the support vector machine (SVM) classifiers using modified particle swarm optimization (PSO) algorithm and their ensembles has been considered. Solving this problem would allow fulfilling the high-precision data classification, especially Big Data classification, with the acceptable time expenditures. The modified PSO algorithm conducts a simultaneous search of the type of kernel functions, the parameters of the kernel function and the value of the regularization parameter for the SVM classifier. The idea of particles' «regeneration» served as the basis for the modified PSO algorithm. In the implementation of this algorithm, some particles change the type of their kernel function to the one which corresponds to the particle with the best value of the classification accuracy. The offered PSO algorithm allows reducing the time expenditures for the developed SVM classifiers, which is very important for Big Data classification problem. In most cases such SVM classifier provides the high quality of data classification. In exceptional cases the SVM ensembles based on the decorrelation maximization algorithm for the different strategies of the decision-making on the data classification and the majority vote rule can be used. Also, the two-level SVM classifier has been offered. This classifier works as the group of the SVM classifiers at the first level and as the SVM classifier on the base of the modified PSO algorithm at the second level. The results of experimental studies confirm the efficiency of the offered approaches for Big Data classification.

Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classification. The numerous spectral bands of hyperspectral images contain rich spectral features but also bring issues of noise and redundancy. To improve the spatial resolution and fully extract spatial and spectral features, this article proposes an improved feature enhancement and extraction model (IFEE) using spatial feature enhancement and attention-guided bidirectional sequential spectral feature extraction for hyperspectral image classification. The adaptive guided filtering is introduced to highlight details and edge features in hyperspectral images. Then, an image enhancement module composed of two-dimensional convolutional neural networks is used to improve the resolution of the image after adaptive guidance filtering and provide a high-resolution image with key features emphasized for the subsequent feature extraction module. The proposed spectral attention mechanism helps to extract more representative spectral features, emphasizing useful information while suppressing the interference of noise. Experimental results show that our method outperforms other comparative methods even with very few training samples.

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.

With numerous and contiguous spectral bands acquired from visible light (400?1,000 nm) to (near) infrared (1,000?1,700 nm and over), hyperspectral imaging (HSI) can potentially identify different objects by detecting minor changes in temperature, moisture, and chemical content. As a result, HSI has been widely applied in a number of application areas, including remote sensing [1]. HSI data contains two-dimensional (2-D) spatial and one-dimensional spectral information, and naturally forms a three-dimensional (3-D) hypercube with a high spectral resolution in nanometers that enables robust discrimination of ground features. However, new challenges arise in dealing with extremely large data sets. For a hypercube with relatively small spatial dimension of 600 ? 400 pixels at 16 bits-per-band-per-pixel, the data volume becomes 120 MB for 250 spectral bands. In some cases, this large data volume can be linearly increased when multiple hypercubes are acquired across time to monitor system dynamics in consecutive time instants. When the ratio between the feature dimension (spectral bands) and the number of data samples (in vector-based pixels) is vastly different, high-dimensional data suffers from the well-known curse of dimensionality. For feature extraction and dimensionality reduction, principal components analysis (PCA) is widely used in HSI [2], where the number of extracted components is significantly reduced compared to the original feature dimension, i.e., the number of spectral bands. For effective analysis of large-scale data in HSI, conventional PCA faces three main challenges:

Remote sensing Hyperspectral Image (HSI) comprises significant information about the earth’s surface which is actually acquired by hundred of narrow and adjacent spectral bands. The intended performance of classification accuracy does not attain due to the volume of the original HSI dataset and the enormous quantity of spectral bands. As such, dimensionality reduction approaches using feature extraction and selection are typically adopted to enhance classification performance. The unsupervised Principal Component Analysis (PCA), as well as the supervised Linear Discriminant Analysis (LDA), are commonly used as linear feature extraction methods for feature subspace detection. However, due to considering the effects of global variation, both PCA and LDA fail to extract local characteristics of HSI. In this paper, we propose a segmented LDA-based (SLDA) feature extraction where we apply the LDA in a segmented way to extract better local characteristics as well as global characteristics from the HSI. Per-pixel classification using a Support Vector Machine (SVM) is applied to our proposed SLDA method, PCA, Segmented-PCA (SPCA), and LDA on the Indian Pines agricultural HSI dataset. The experimental results show that the overall classification performance of SLDA (90.60%) remarkably outperforms all the other investigated methods: PCA (85.55%), SPCA (86.96%), LDA (86.45%), and the complete original dataset without employing any feature reduction method (83.10%). The proposed SLDA also requires the least amount of space complexity in different implementation scenarios.

Introduction/Background*The most important prognostic factor for the survival of advanced-stage ovarian cancer is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumours would be of...

Because of the character of hyperspectral remote sensing data,it is necessary and urgent to develop hyperspectral data process algorithm.In this paper,some hyperspectral data process algorithms for feature selection and feature extraction was discussed,and its advantage disadvantage was analyzed.In particular,we studied derivative spectral algorithm and put forward quad-encoding algorithm as the improved the binary encoding algorithm.Using the algorithms this paper proposed we extract spectral absorption parameter.The experiments have demonstrated that quad-encoding algorithm has the better performance than binary encoding on hyperspectral data,and for derivative spectrum it is effective to indicate validate feature for objects when its rank is higher.

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.

Process Monitoring and Fault Detection using Empirical Mode Decomposition and Singular Spectrum Analysis

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

The Hyperspectral Image (HSI) is a great source of information for observing the earth’s elements due to its numerous narrow and continuous spectral wavelength bands. There are some key difficulties, such as the fact that the image bands are spectrally and spatially highly correlated, and the ‘curse of dimensionality’ in using the original HSI for classification. Band (dimensionality/feature) reduction is necessary to improve classification performance through feature extraction and selection. As such, this paper proposes a hybrid HSI classification paradigm, termed Hybrid-2DNET. Specifically, our proposed Hybrid-2DNET incorporates factor analysis-based feature extraction along the Minimum-Redundancy-Maximum-Relevance (mRMR)-based feature selection criterion and a 2D-wavelet Convolutional Neural Network (CNN) to reduce both spectral and spatial dimensionalities. The experimental analyses performed on three remote sensings HSI datasets, namely Indian Pines, University of Pavia, and Salinas Scene, manifest that our proposed Hybrid-2DNET outperforms the handcrafted and deep learning methods.