1D-CONVOLUTIONAL AUTOENCODER BASED HYPERSPECTRAL DATA COMPRESSION

Abstract. Hyperspectral sensor technology has been advancing in recent years and become more practical to tackle a variety of applications. The arising issues of data transmission and storage can be addressed with the help of compression. To minimize the loss of important information, high spectral correlation between adjacent bands is exploited. In this paper, we introduce an approach to compress hyperspectral data based on a 1D-Convolutional Autoencoder. Compression is achieved through reducing correlation by transforming the spectral signature into a low-dimensional space, while simultaneously preserving the significant features. The focus lies on compression of the spectral dimension. The spatial dimension is not used in the compression in order not to falsify correlation between the spectral dimension and accuracy of the reconstruction. The proposed 1D-Convolutional Autoencoder efficiently finds and extracts features relevant for compression. Additionally, it can be exploited as a feature extractor or for dimensionality reduction. The hyperspectral data sets Greding Village and Pavia University were used for the training and the evaluation process. The reconstruction accuracy is evaluated using the Signal to Noise Ratio and the Spectral Angle. Additionally, a land cover classification using a multi-class Support Vector Machine is used as a target application. The classification performance of the original and reconstructed data are compared. The reconstruction accuracy of the 1D-Convolutional Autoencoder outperforms the Deep Autoencoder and Nonlinear Principal Component Analysis for the used metrics and for both data sets using a fixed compression ratio.

Download Full-text

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.7.445 ◽

2021 ◽

Vol 87 (6) ◽

pp. 445-455

Author(s):

Yi Ma ◽

Zezhong Zheng ◽

Yutang Ma ◽

Mingcang Zhu ◽

Ran Huang ◽

...

Keyword(s):

Manifold Learning ◽

Nearest Neighbor ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Training Data ◽

Support Vector ◽

Data Sets ◽

K Nearest Neighbor ◽

Data Set ◽

Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

Download Full-text

Deep Convolutional Neural Networks for Hyperspectral Image Classification

Journal of Sensors ◽

10.1155/2015/258619 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 419

Author(s):

Wei Hu ◽

Yangyu Huang ◽

Li Wei ◽

Fan Zhang ◽

Hengchao Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Hyperspectral Image ◽

Image Data ◽

Classification Performance ◽

Spectral Signature ◽

Support Vector ◽

Data Sets ◽

Deep Convolutional Neural Networks ◽

Visual Tasks

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

Download Full-text

Spectral-spatial destriping of hyperspectral image via correntropy based sparse representation and unidirectional Huber–Markov random fields

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691317500564 ◽

2017 ◽

Vol 15 (06) ◽

pp. 1750056 ◽

Cited By ~ 1

Author(s):

Yulong Wang ◽

Yuan Yan Tang ◽

Luoqing Li

Keyword(s):

Sparse Representation ◽

Random Fields ◽

Markov Random Fields ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Data Sets ◽

Spectral Correlation ◽

Markov Random ◽

Augmented Lagrange Multiplier

This paper presents a novel destriping method for hyperspectral images. Most of the previous destriping methods regard only the corrupted subimage as an isolated image and fail to consider the high spectral correlation between the subimages in different bands. This may impede their performance of removing striping noises. The proposed method takes advantage of both spectral and spatial information to contribute to the process of striping noise reduction. Firstly, a correntropy-based sparse representation (CSR) model is utilized to learn the high spectral correlation between the subimages in different bands. Then the spatial information of the target subimage with striping noise is incorporated into the CSR model with a unidirectional Huber–Markov random field prior. We devise an Augmented Lagrange Multiplier type of algorithm to efficiently compute the destriped results. The experimental results on two real-world hyperspectral data sets demonstrate the effectiveness of the proposed method.

Download Full-text

Online teaching quality evaluation model based on support vector machine and decision tree

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189218 ◽

2020 ◽

pp. 1-11

Author(s):

Jingwen Hou

Keyword(s):

Online Teaching ◽

Large Scale ◽

Quality Evaluation ◽

Dimensional Space ◽

Teaching Quality ◽

Support Vector ◽

Small Scale ◽

Data Sets ◽

Large Scale Data ◽

Scale Data

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.

Download Full-text

Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification

Remote Sensing ◽

10.3390/rs11030223 ◽

2019 ◽

Vol 11 (3) ◽

pp. 223 ◽

Cited By ~ 22

Author(s):

Kaiqiang Zhu ◽

Yushi Chen ◽

Pedram Ghamisi ◽

Xiuping Jia ◽

Jón Atli Benediktsson

Keyword(s):

Network Architecture ◽

Hyperspectral Image ◽

Principal Component ◽

Hyperspectral Data ◽

Support Vector ◽

Data Sets ◽

Spatial Classification ◽

Training Samples ◽

Vector Machines ◽

Kernel Support Vector Machines

Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.

Download Full-text

Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2013/718783 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 5

Author(s):

Mingli Ding ◽

Dongmei Yang ◽

Xiaobing Li

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Optimization Technique ◽

Large Data ◽

Wireless Sensor ◽

Sensor Technology ◽

Twin Support Vector Machine ◽

Support Vector ◽

Data Sets ◽

Training Procedure

Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN.

Download Full-text

Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data

Remote Sensing ◽

10.3390/rs12030516 ◽

2020 ◽

Vol 12 (3) ◽

pp. 516 ◽

Cited By ~ 7

Author(s):

Anita Sabat-Tomala ◽

Edwin Raczko ◽

Bogdan Zagajewski

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Hyperspectral Data ◽

Machine Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Data Sets ◽

Natural Ecosystem ◽

Data Set ◽

Calamagrostis Epigejos

Invasive and expansive plant species are considered a threat to natural biodiversity because of their high adaptability and low habitat requirements. Species investigated in this research, including Solidago spp., Calamagrostis epigejos, and Rubus spp., are successfully displacing native vegetation and claiming new areas, which in turn severely decreases natural ecosystem richness, as they rapidly encroach on protected areas (e.g., Natura 2000 habitats). Because of the damage caused, the European Union (EU) has committed all its member countries to monitor biodiversity. In this paper we compared two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to identify Solidago spp., Calamagrostis epigejos, and Rubus spp. on HySpex hyperspectral aerial images. SVM and RF are reliable and well-known classifiers that achieve satisfactory results in the literature. Data sets containing 30, 50, 100, 200, and 300 pixels per class in the training data set were used to train SVM and RF classifiers. The classifications were performed on 430-spectral bands and on the most informative 30 bands extracted using the Minimum Noise Fraction (MNF) transformation. As a result, maps of the spatial distribution of analyzed species were achieved; high accuracies were observed for all data sets and classifiers (an average F1 score above 0.78). The highest accuracies were obtained using 30 MNF bands and 300 sample pixels per class in the training data set (average F1 score > 0.9). Lower training data set sample sizes resulted in decreased average F1 scores, up to 13 percentage points in the case of 30-pixel samples per class.

Download Full-text

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666200428120310 ◽

2020 ◽

Vol 23 (8) ◽

pp. 805-813

Author(s):

Ai Jiang ◽

Peng Xu ◽

Zhenda Zhao ◽

Qizhao Tan ◽

Shang Sun ◽

...

Keyword(s):

Signaling Pathway ◽

Microarray Data ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Mapk Signaling ◽

Functional Enrichment ◽

Joint Disease ◽

Support Vector ◽

Diagnostic Model ◽

Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

Download Full-text

Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

SN Applied Sciences ◽

10.1007/s42452-020-03999-y ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Seyed Muhammad Hossein Mousavi ◽

S. Younes Mirinezhad

Keyword(s):

Neural Network ◽

Facial Expression ◽

Facial Expression Recognition ◽

Depth Image ◽

Sensor Technology ◽

Support Vector ◽

Expression Recognition ◽

Face Database ◽

Depth Data ◽

Color Depth

AbstractThis study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

Download Full-text

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽

10.3390/s21030742 ◽

2021 ◽

Vol 21 (3) ◽

pp. 742

Author(s):

Canh Nguyen ◽

Vasit Sagan ◽

Matthew Maimaitiyiming ◽

Maitiniyazi Maimaitijiang ◽

Sourav Bhadra ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Early Detection ◽

Convolutional Neural Network ◽

Near Infrared ◽

Hyperspectral Data ◽

Viral Diseases ◽

Support Vector ◽

Spectral Features ◽

Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

Download Full-text