Hyperspectral Imaging for Color Adulteration Detection in Red Chili

Khan, Muhammad Hussain; Saleem, Zainab; Sohaib, Ahmed; Ayaz, Hamail; Mazzara, Manuel

doi:10.3390/app10175955

Cited by 25 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Higher-order derivatives cause a reduction in signal-to-noise ratio (SNR). Savitzky Golay filter is one of the mathematical derivative methods that are better than others due to its soothing property [ 42 , 43 , 44 , 45 ]. The Savitzky Golay filter is based on polynomial fitting on data points depending upon window size.…”

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging for Bloodstain Identification

Zulfiqar

Ahmad

Sohaib

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Blood is key evidence to reconstruct crime scenes in forensic sciences. Blood identification can help to confirm a suspect, and for that reason, several chemical methods are used to reconstruct the crime scene however, these methods can affect subsequent DNA analysis. Therefore, this study presents a non-destructive method for bloodstain identification using Hyperspectral Imaging (HSI, 397–1000 nm range). The proposed method is based on the visualization of heme-components bands in the 500–700 nm spectral range. For experimental and validation purposes, a total of 225 blood (different donors) and non-blood (protein-based ketchup, rust acrylic paint, red acrylic paint, brown acrylic paint, red nail polish, rust nail polish, fake blood, and red ink) samples (HSI cubes, each cube is of size 1000 × 512 × 224, in which 1000 × 512 are the spatial dimensions and 224 spectral bands) were deposited on three substrates (white cotton fabric, white tile, and PVC wall sheet). The samples are imaged for up to three days to include aging. Savitzky Golay filtering has been used to highlight the subtle bands of all samples, particularly the aged ones. Based on the derivative spectrum, important spectral bands were selected to train five different classifiers (SVM, ANN, KNN, Random Forest, and Decision Tree). The comparative analysis reveals that the proposed method outperformed several state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Hyperspectral Imaging for Bloodstain Identification

Zulfiqar

Ahmad

Sohaib

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The images thus obtained are usually generated by a pre-configured HSI camera installed on either mobile (e.g. satellites, drones, air-crafts) or static (e.g., indoor, rooms, labs) setups depending upon the problem at hand [2]- [11]. Thereby, HSI sensors gather a huge amount of data from hundreds of contiguous spectral bands [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Khan

Mazzara

Distefano

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixel-wise classification challenging for conventional methods. To deal with nonlinearity issues in Hyperspectral Image Classification (HSIC), Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting Attention-fused Hybrid Network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using highlevel features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models.

show abstract

“…The 3D hyperspectral images (HSIs) are characterized by their rich spectral information, which can be utilized to identify the materials by their unique reflective spectra [2]. In this way, the HSIs have gained wide applications in addressing the issue of geological application, military rescue and mineral exploration [3]. Among these applications, anomaly detection seeks to discriminate the abnormal observations without the prior information about the target.…”

Section: Introductionmentioning

confidence: 99%

Matrix Autoregressive Model for Hyperspectral Anomaly Detection

Wang

Sun

Zhu

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

For anomaly detection in hyperspectral imagery, the scene can be treated as a combination of the background and the anomalies. Once a pure background hyperspectral image (HSI) is obtained, the anomalies can be easily located. This paper detects the anomalies via a matrix autoregressive model (MARM) to reconstruct the background HSI. Specifically, some informative and discriminative bands are firstly selected and come into a new HSI with less bands. Secondly, the new HSI can be treated as a collection of profiles in the row direction. Based on this, the background can be regularly reconstructed via the MARM. The regressive model not only respects the original matrix structure in the row profiles, but also utilizes both the spatial and spectral correlation for the detection process. Finally, the classical Reed Xiaoli detector is applied to the difference cube between the band selected HSI and the HSI reconstructed by MARM, achieving a final detection map with higher accuracy. Experimental results and data analysis on four different sensors captured datasets with different resolutions have validated the effectiveness of the proposed method.

show abstract

Hyperspectral Imaging for Color Adulteration Detection in Red Chili

Cited by 25 publications

References 31 publications

Hyperspectral Imaging for Bloodstain Identification

Hyperspectral Imaging for Bloodstain Identification

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Matrix Autoregressive Model for Hyperspectral Anomaly Detection

Contact Info

Product

Resources

About