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3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes
[chapter]
Lecture Notes in Computer Science
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This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. ...
The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. ...
Acknowledgments This study was supported in part by the National Science Council of Taiwan under Project No. NSC-95-2752-M-008-005-PAE. ...
doi:10.1007/978-3-540-74198-5_33
fatcat:l5ksx75gtvcjfgub5i4cso3l3m
Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with Deep Learning
2019
Remote Sensing
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The proposed method first extracts sea ice texture information by the gray-level co-occurrence matrix (GLCM). ...
The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/rs11182170
fatcat:cypop3a5obgevf33rv7jboepiu
Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images
2012
EURASIP Journal on Advances in Signal Processing
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MA Roula and the Pathology department team at the Queen's university of Belfast under the direction of Prof. Hamilton for kindly providing maging data used in this study. ...
on the ability of co-occurrence probability statistics; Kiema [25] examined the gray-level co-occurrence based texture image fused to thematic mapper (TM) imagery to expand the object feature base to ...
Like in the GLCM, in which the co-occurrence matrix is computed for each band, we can apply SSGLDM for each subset of bands in the image. ...
doi:10.1186/1687-6180-2012-118
fatcat:3al33wa3efg47flrzgwn4xvupy
ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU
2017
Sensors
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In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. ...
In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier ...
Acknowledgments: The research has been carried out in partnership with industrial companies, within the framework of the National Research Project (PON) "Apulia Space", ID PON03PE_00067_6. ...
doi:10.3390/s17051160
pmid:28534816
pmcid:PMC5470906
fatcat:pqgc4ajbzfbyxhommtq2wuqzam
A Novel Clustering-Based Feature Representation for the Classification of Hyperspectral Imagery
2014
Remote Sensing
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Meanwhile, the performance of the MCH is compared to three other widely used spatial features: the gray-level co-occurrence matrix (GLCM), the 3D wavelet texture, and differential morphological profiles ...
In this study, a new clustering-based feature extraction algorithm is proposed for the spectral-spatial classification of hyperspectral imagery. ...
Liangpei Zhang provided the background knowledge and contributed in the revision of the paper.
Conflicts of Interest The authors declare no conflict of interest. ...
doi:10.3390/rs6065732
fatcat:ph3slgoc4nfefimojbpgp5nvtu
Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
2020
Applied Sciences
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A total of 60 HSI cubes are acquired using Fx 10 Hyperspectral sensor. ...
Recently, Hyperspectral Imaging (HSI) has been used for the classification and identification of minced meat types. ...
[23] experimented on an entire HSI cube of real salmon fillets through several feature descriptors such as Gray-level Co-occurrence Matrix (GLCM), variogram, Histograms of Oriented Gradients (HOG), ...
doi:10.3390/app10217783
fatcat:b7l2coyuyvf27l2zr5ldmkjylq
Low-bit rate exploitation-based lossy hyperspectral image compression
2010
Journal of Applied Remote Sensing
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Accordingly, a direct application of 2D-to-3D compression techniques to hyperspectral image cubes without taking precaution may result in significant loss of crucial spectral information provided by subtle ...
Since a hyperspectral image can be viewed as a 3-dimensional (3D) image cube, many efforts have been devoted to extending 2D image compression techniques to perform 3D image compression on hyperspectral ...
The authors would also like to acknowledge the use of the Kakadu software developed by Dr. David Taubman. In addition, C. ...
doi:10.1117/1.3530429
fatcat:2f72oxaiwbd2tju45umqfy5zyi
Hardwood Species Classification with Hyperspectral Microscopic Images
2019
Journal of Spectroscopy
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A SOC710VP hyperspectral stereomicroscope was used to acquire microscopic images of a hardwood cross section. In these microscopic images, each part's spectral features are discussed. ...
We found that the spectral divisibility of wood vessels' peripheral and central regions in the hyperspectral microscopic images can be used for hardwood species recognition. ...
traditional approach to image texture classification applied for the identification of wood species based on GLCM (Gray-Level Co-Occurrence Matrix), which will not be described here. e method described ...
doi:10.1155/2019/2039453
fatcat:ide37o2awvatppp67i3dm46bj4
Visible/near-infrared hyperspectral imaging for beef tenderness prediction
2008
Computers and Electronics in Agriculture
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Gray-level textural co-occurrence matrix analysis was conducted to extract second-order statistical textural features from the principal component images. ...
The first five principal components explained over 90% of the variance of all spectral bands in the image. ...
Gray-level co-occurrence matrix analysis On the PC images, four gray-level co-occurrence matrix (GLCM) analyses with a distance value of 1 and angles of 0°, 45°, 90°, and 135° were constructed for extracting ...
doi:10.1016/j.compag.2008.05.020
fatcat:roeu65llh5f55e2r5oo2yh5imq
Analysis of hyperspectral fluorescence images for poultry skin tumor inspection
2004
Applied Optics
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The large amount of 1 hyperspectral image data is compressed using a discrete wavelet transform in the spatial domain. ...
The hyperspectral image samples obtained for this poultry tumor inspection contains 65 spectral bands of fluorescence in the visible region of the spectrum at wavelengths ranging from 425 nm to 711 nm. ...
The data produced by hyperspectral imaging sensors constitute a three-dimensional (3D) cube in two spatial and one spectral dimension. ...
doi:10.1364/ao.43.000824
pmid:14960077
fatcat:qvw7j5j23fghlcovogeik65oge
Detection and Classification of Early Decay on Blueberry Based on Improved Deep Residual 3D Convolutional Neural Network in Hyperspectral Images
2020
Scientific Programming
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In addition, aiming at the problem of few samples, this paper proposes a novel strategy to enhance the hyperspectral image sample data, which can improve the training effect. ...
An improved deep residual 3D convolutional neural network (3D-CNN) framework is proposed for hyperspectral images classification so as to realize fast training, classification, and parameter optimization ...
co-occurrence matrix (GLCM), respectively. ...
doi:10.1155/2020/8895875
fatcat:lwv5gqnp5bbczbhvkjjljdqp6q
Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data
2019
Remote Sensing
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Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. ...
This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil ...
; (iii) GLCM: gray-level co-occurrence matrix [52] textural features extracted from the VNIR hyperspectral data; (iv) CHM: canopy height model; (v) VI: vegetation indices; and (vi) PPC: features extracted ...
doi:10.3390/rs11111338
fatcat:gbuptijffvfbhlfjxistgjff44
A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching
2021
Remote Sensing
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Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. ...
Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. ...
On the other hand, methods are applied for HSI matching by extending the 2D feature extraction function into 3D space, such as 3D SIFT [24] , 3D gray level co-occurrence matrix [25] and 3D wavelet transform ...
doi:10.3390/rs13234912
fatcat:7qm6loz64rgwbetboxg6q65hh4
Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat
2018
Meat Science
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Abstract: Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat ...
Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat. ...
This is estimated by using the gray-level co-occurrence matrix (GLCM) (Klette, 2014) . ...
doi:10.1016/j.meatsci.2018.05.020
pmid:29960721
fatcat:l7jy7bed6nd6find5ses62ezdi
Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images
2018
Journal of Imaging
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This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. ...
This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jimaging4050063
fatcat:bp2jclbiazgsfhhby2x4z3jtiq
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