International Science Index

5
10007743
Sparsity-Based Unsupervised Unmixing of Hyperspectral Imaging Data Using Basis Pursuit
Abstract:

Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l1-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l1-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Paper Detail
37
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4
10007400
Classification of Germinatable Mung Bean by Near Infrared Hyperspectral Imaging
Abstract:

Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Paper Detail
83
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3
10003751
Reflectance Imaging Spectroscopy Data (Hyperspectral) for Mineral Mapping in the Orientale Basin Region on the Moon Surface
Abstract:
Mineral mapping on the Moon surface provides the clue to understand the origin, evolution, stratigraphy and geological history of the Moon. Recently, reflectance imaging spectroscopy plays a significant role in identifying minerals on the planetary surface in the Visible to NIR region of the electromagnetic spectrum. The Moon Mineralogy Mapper (M3) onboard Chandrayaan-1 provides unprecedented spectral data of lunar surface to study about the Moon surface. Here we used the M3 sensor data (hyperspectral imaging spectroscopy) for analysing mineralogy of Orientale basin region on the Moon surface. Reflectance spectrums were sampled from different locations of the basin and continuum was removed using ENvironment for Visualizing Images (ENVI) software. Reflectance spectra of unknown mineral composition were compared with known Reflectance Experiment Laboratory (RELAB) spectra for discriminating mineralogy. Minerals like olivine, Low-Ca Pyroxene (LCP), High-Ca Pyroxene (HCP) and plagioclase were identified. In addition to these minerals, an unusual type of spectral signature was identified, which indicates the probable Fe-Mg-spinel lithology in the basin region.
Paper Detail
1805
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2
10003454
Hyperspectral Imaging and Nonlinear Fukunaga-Koontz Transform Based Food Inspection
Abstract:
Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.
Paper Detail
780
downloads
1
6018
Enhancement of m-FISH Images using Spectral Unmixing
Abstract:
Breast carcinoma is the most common form of cancer in women. Multicolour fluorescent in-situ hybridisation (m-FISH) is a common method for staging breast carcinoma. The interpretation of m-FISH images is complicated due to two effects: (i) Spectral overlap in the emission spectra of fluorochrome marked DNA probes and (ii) tissue autofluorescence. In this paper hyper-spectral images of m-FISH samples are used and spectral unmixing is applied to produce false colour images with higher contrast and better information content than standard RGB images. The spectral unmixing is realised by combinations of: Orthogonal Projection Analysis (OPA), Alterating Least Squares (ALS), Simple-to-use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) and VARIMAX. These are applied on the data to reduce tissue autofluorescence and resolve the spectral overlap in the emission spectra. The results show that spectral unmixing methods reduce the intensity caused by tissue autofluorescence by up to 78% and enhance image contrast by algorithmically reducing the overlap of the emission spectra.
Paper Detail
1074
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