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Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 26990

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Dr. Mohammed A. A. Al-qaness

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School of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Interests: artificial intelligence; swarm intelligence; deep learning; data science; remote sensing; hyperspectral image processing;
Special Issues, Collections and Topics in MDPI journals

Dr. Mohamed Abd Elaziz

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Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Interests: methahursitics; optimization algorithms; machine learning; image classification

Dr. Ahmed A. Ewees

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Department of Computer, Damietta University, Damietta 34511, Egypt
Interests: computer vision; deep learning; data science; swarm intelligence; image segmentation; global optimization;
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Dr. Laith Abualigah

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Faculty of Computer Sciences and Informatics, Amman Arab University, 11953 Amman, Jordan
Interests: swarm intelligence; soft computing; data science; deep learning; image processing;
Special Issues, Collections and Topics in MDPI journals

Dr. Xiongwu Xiao

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: SLAM and real-time photogrammetry; multi-source data fusion; 3D reconstruction; building extraction and intelligent 3D mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We lunch this special issue on “Hyperspectral images processing and classification using Artificial Intelligence (AI) techniques”. Hyperspectral imaging was employed for remote sensing and earth observation since may years. There are various methods applied for Hyperspectral imaging processing and applications, such as traditional computer vison methods and machine learning algorithms. In recent years, the advances of deep learning methods have been leveraged for Hyperspectral imaging. More so, the metaheuristic (MH) optimization algorithms, including the swarm intelligence optimization algorithms such as particle swarm optimization (PSO) algorithm, artificial bee colony (ABC) algorithm (ABC), and others, have been successfully employed in image processing applications. In this Special Issue, we try to provide a forum for the publication of articles describing the applications of artificial intelligence methods, including deep learning, metaheuristic optimization algorithms in the field of Hyperspectral imaging. The main goal of this Special Issue is to capture recent contributions of high-quality papers focusing on Hyperspectral imaging, including segmentations, noise reduction, classification and analysis. Therefore, we invite colleagues to contribute original research papers as well as review papers that focus on the Hyperspectral imaging analysis, using advance AI methods, such as deep learning approaches, and bio-inspired optimization algorithms. The topics of this Special Issue include (but are not limited to) the following:

Hyperspectral images classification;

Hyperspectral data visualization;

Applications of hyperspectral imaging;

Metaheuristic optimization algorithms for Hyperspectral imaging;

Hyperspectral images segmentation;

Hyperspectral imaging for earth observation;

Hyperspectral imaging for agriculture.

Dr. Mohammed A. A. Al-qaness
Dr. Mohamed Abd Elaziz
Dr. Ahmed A. Ewees
Dr. Laith Abualigah
Dr. Xiongwu Xiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

hyperspectral imaging
remote sensing
earth observation
deep learning
metaheuristic algorithms
swarm intelligence
image segmentation

Published Papers (9 papers)

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Research

18 pages, 4896 KiB

Open AccessArticle

Random Shuffling Data for Hyperspectral Image Classification with Siamese and Knowledge Distillation Network

by Zhen Yang, Ying Cao, Xin Zhou, Junya Liu, Tao Zhang and Jinsheng Ji

Remote Sens. 2023, 15(16), 4078; https://doi.org/10.3390/rs15164078 - 18 Aug 2023

Viewed by 874

Abstract

Hyperspectral images (HSIs) are characterized by hundreds of spectral bands. The goal of HSI is to associate the pixel with a corresponding category label by analyzing subtle differences in the spectrum. Due to their excellent local context modeling capabilities, Convolutional Neural Network (CNN)-based methods are often adopted to complete the classification task. To verify whether the patch-data-based CNN methods depend on the homogeneity of patch data during the training process in HSI classification, we designed a random shuffling strategy to disrupt the data homogeneity of the patch data, which is randomly assigning the pixels from the original dataset to other positions to form a new dataset. Based on this random shuffling strategy, we propose a sub-branch to extract features on the reconstructed dataset and fuse the loss rates (RFL). The loss rate calculated by RFL in the new patch data is cross combined with the loss value calculated by another sub-branch in the original patch data. Moreover, we construct a new hyperspectral classification network based on the Siamese and Knowledge Distillation Network (SKDN) that can improve the classification accuracy on randomly shuffled data. In addition, RFL is introduced into the original model for hyperspectral classification tasks in the original dataset. The experimental results show that the improved model is also better than the original model, which indicates that RFL is effective and feasible. Experiments on four real-world datasets show that, as the proportion of randomly shuffling data increases, the latest patch-data-based CNN methods cannot extract more abundant local contextual information for HSI classification, while the proposed sub-branch RFL can alleviate this problem and improve the network’s recognition ability. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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18 pages, 21906 KiB

Open AccessArticle

Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method

by Tan Guo, Fulin Luo, Yule Duan, Xinjian Huang and Guangyao Shi

Remote Sens. 2023, 15(14), 3608; https://doi.org/10.3390/rs15143608 - 19 Jul 2023

Viewed by 761

Abstract

Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some cases, cannot accurately distinguish the target pixels from variable background pixels via one-round of the detection process. To alleviate the problem and make full use of the latent discriminate characteristics in different spectral bands and the representation residual, this paper proposes a level-wise band-partition-based hierarchical representation residual feature (LBHRF) learning method for HTD with a parallel and cascaded hybrid structure. Specifically, the LBHRR method proposes to partition and fuse different levels of sub-band spectra combinations, and take full advantages of the discriminate information in representation residuals from different levels of band-partition. The highlights of this work include three aspects. First, the original full spectral bands are partitioned in a parallel level-wise manner to obtain the augmented representation residual feature through level-wise band-partition-based representation residual learning, such that the global spectral integrity and contextual information of local adjacent sub-bands are flexibly fused. Second, the SoftMax transformation, pooling operation, and augmented representation residual feature reuse among different layers are equipped in cascade to enhance the learning ability of the nonlinear and discriminant hierarchical representation residual features of the method. Third, a hierarchical representation residual feature-based HTD method is developed in an efficient stepwise learning manner instead of back-propagation optimization. Experimental results on several HSI datasets demonstrate that the proposed model can yield promising detection performance in comparison to some state-of-the-art counterparts. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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22 pages, 8752 KiB

Open AccessArticle

Hyperspectral Image Classification Based on Fusing S³-PCA, 2D-SSA and Random Patch Network

by Huayue Chen, Tingting Wang, Tao Chen and Wu Deng

Remote Sens. 2023, 15(13), 3402; https://doi.org/10.3390/rs15133402 - 04 Jul 2023

Cited by 37 | Viewed by 3302

Abstract

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S³-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S³-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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21 pages, 4487 KiB

Open AccessArticle

Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire

by Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, Stefania Amici, Sarathchandrakumar Thottuchirayil Sasidharan, Haytham Fayek and Pier Marzocca

Remote Sens. 2023, 15(3), 720; https://doi.org/10.3390/rs15030720 - 26 Jan 2023

Cited by 32 | Viewed by 6966

Abstract

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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22 pages, 6259 KiB

Open AccessArticle

A Hypered Deep-Learning-Based Model of Hyperspectral Images Generation and Classification for Imbalanced Data

by Hasan A. H. Naji, Tianfeng Li, Qingji Xue and Xindong Duan

Remote Sens. 2022, 14(24), 6406; https://doi.org/10.3390/rs14246406 - 19 Dec 2022

Cited by 5 | Viewed by 2068

Abstract

Recently, hyperspectral image (HSI) classification has become a hot topic in the geographical images research area. Sufficient samples are required for image classes to properly train classification models. However, a class imbalance problem has emerged in hyperspectral image (HSI) datasets as some classes do not have enough samples for training, and some classes have many samples. Therefore, the performance of classifiers is likely to be biased toward the classes with the largest samples, and this can lead to a decrease in the classification accuracy. Therefore, a new deep-learning-based model is proposed for hyperspectral images generation and classification of imbalanced data. Firstly, the spectral features are extracted by a 1D convolutional neural network, whereas a 2D convolutional neural network extracts the spatial features and the extracted spatial features and spectral features are catenated into a stacked spatial–spectral feature vector. Secondly, an autoencoder model was developed to generate synthetic images for minority classes, and the image samples were balanced. The GAN model is applied to determine the synthetic images from the real ones and then enhancing the classification performance. Finally, the balanced datasets are fed to a 2D CNN model for performing classification and validating the efficiency of the proposed model. Our model and the state-of-the-art classifiers are evaluated by four open-access HSI datasets. The results showed that the proposed approach can generate better quality samples for rebalancing datasets, which in turn noticeably enhances the classification performance compared to the existing classification models. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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32 pages, 10329 KiB

Open AccessArticle

A Hybrid Classification of Imbalanced Hyperspectral Images Using ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest

by Debaleena Datta, Pradeep Kumar Mallick, Annapareddy V. N. Reddy, Mazin Abed Mohammed, Mustafa Musa Jaber, Abed Saif Alghawli and Mohammed A. A. Al-qaness

Remote Sens. 2022, 14(19), 4853; https://doi.org/10.3390/rs14194853 - 28 Sep 2022

Cited by 8 | Viewed by 2096

Abstract

Hyperspectral image (HSI) analysis generally suffers from issues such as high dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for artificially balanced datasets. The existing conventional data imbalance removal techniques and forest classifiers lack a more efficient approach to dealing with the aforementioned issues. In this study, we propose a novel hybrid methodology ADASYN-enhanced subsampled multi-grained cascade forest (ADA-Es-gcForest) which comprises four folds: First, we extracted the most discriminative global spectral features by reducing the vast dimensions, i.e., the redundant bands using principal component analysis (PCA). Second, we applied the subsampling-based adaptive synthetic minority oversampling method (ADASYN) to augment and balance the dataset. Third, we used the subsampled multi-grained scanning (Mg-sc) to extract the minute local spatial–spectral features by adaptively creating windows of various sizes. Here, we used two different forests—a random forest (RF) and a complete random forest (CRF)—to generate the input joint-feature vectors of different dimensions. Finally, for classification, we used the enhanced deep cascaded forest (CF) that improvised in the dimension reduction of the feature vectors and increased the connectivity of the information exchange between the forests at the different levels, which elevated the classifier model’s accuracy in predicting the exact class labels. Furthermore, the experiments were accomplished by collecting the three most appropriate, publicly available his landcover datasets—the Indian Pines (IP), Salinas Valley (SV), and Pavia University (PU). The proposed method achieved 91.47%, 98.76%, and 94.19% average accuracy scores for IP, SV, and PU datasets. The validity of the proposed methodology was testified against the contemporary state-of-the-art eminent tree-based ensembled methods, namely, RF, rotation forest (RoF), bagging, AdaBoost, extreme gradient boost, and deep multi-grained cascade forest (DgcForest), by simulating it numerically. Our proposed model achieved correspondingly higher accuracies than those classifiers taken for comparison for all the HS datasets. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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18 pages, 5521 KiB

Open AccessArticle

Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction

by Hongda Li, Jian Cui, Xinle Zhang, Yongqi Han and Liying Cao

Remote Sens. 2022, 14(18), 4579; https://doi.org/10.3390/rs14184579 - 13 Sep 2022

Cited by 13 | Viewed by 3097

Abstract

Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R²) of up to 0.979 solves the problem of mixed pixel decomposition. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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23 pages, 4850 KiB

Open AccessArticle

A Comparative Analysis of Swarm Intelligence and Evolutionary Algorithms for Feature Selection in SVM-Based Hyperspectral Image Classification

by Yiqun Shang, Xinqi Zheng, Jiayang Li, Dongya Liu and Peipei Wang

Remote Sens. 2022, 14(13), 3019; https://doi.org/10.3390/rs14133019 - 23 Jun 2022

Cited by 14 | Viewed by 2087

Abstract

Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and Swarm Intelligence and Evolutionary Algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter–wrapper (F–W) framework that can improve the SIEAs’ performance; and (2) to apply ten SIEAs under the F–W framework (F–W–SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs’ performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F–W–Genetic Algorithm (F–W–GA) and F–W–Grey Wolf Optimizer (F–W–GWO) have the strongest optimization abilities, while the F–W–GWO requires the least runtime among the ten. The F–W–Marine Predators Algorithm (F–W–MPA) is second only to the two and slightly better than F–W–Differential Evolution (F–W–DE). The F–W–Ant Lion Optimizer (F–W–ALO), F–W–I-Ching Divination Evolutionary Algorithm (F–W–IDEA), and F–W–Whale Optimization Algorithm (F–W–WOA) have the middle optimization abilities, and F–W–IDEA takes the most runtime. Moreover, the F–W–SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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21 pages, 15231 KiB

Open AccessArticle

Meta-Learner Hybrid Models to Classify Hyperspectral Images

by Dalal AL-Alimi, Mohammed A. A. Al-qaness, Zhihua Cai, Abdelghani Dahou, Yuxiang Shao and Sakinatu Issaka

Remote Sens. 2022, 14(4), 1038; https://doi.org/10.3390/rs14041038 - 21 Feb 2022

Cited by 18 | Viewed by 2921

Abstract

Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance. Full article

(This article belongs to the Special Issue Hyperspectral Images Processing and Classification Using Artificial Intelligence (AI) Techniques)

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