Mesfer Al Duhayyim^1,*, Hadeel Alsolai², Siwar Ben Haj Hassine³, Jaber S. Alzahrani⁴, Ahmed S. Salama⁵, Abdelwahed Motwakel⁶, Ishfaq Yaseen⁶, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3167-3181, 2023, DOI:10.32604/cmc.2023.033054 - 31 October 2022

Abstract Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the… More >

Lin Yuan¹, Jingcheng Zhang^2,*, Quan Deng², Yingying Dong³, Haolin Wang², Xiankun Du²

Phyton-International Journal of Experimental Botany, Vol.92, No.2, pp. 611-628, 2023, DOI:10.32604/phyton.2022.023662 - 12 October 2022

Abstract Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests. In most previous studies, the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice. If a detection system can be established with a few bands and a relatively simple logic, it would be of great significance for application. This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat, i.e., powdery mildew, yellow rust… More >

Jingbo Zhang, Yanjun Zhang, Xingjuan Cai^*, Liping Xie^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 293-316, 2023, DOI:10.32604/cmes.2022.020426 - 24 August 2022

Abstract Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect… More >

S. Prasanna Bharathi^1,2, S. Srinivasan^1,*, G. Chamundeeswari¹, B. Ramesh¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 579-594, 2023, DOI:10.32604/csse.2023.027592 - 16 August 2022

Abstract Now a days, Remote Sensing (RS) techniques are used for earth observation and for detection of soil types with high accuracy and better reliability. This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics. There are a few challenges that is present in soil classification using image enhancement such as, locating and plotting soil boundaries, slopes, hazardous areas, drainage condition, land use, vegetation etc. There are some traditional approaches which involves few drawbacks such as, manual involvement which results in inaccuracy due to human interference, time… More >

B. Chellapraba^1,*, D. Manohari², K. Periyakaruppan³, M. S. Kavitha⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2353-2366, 2023, DOI:10.32604/iasc.2023.029923 - 19 July 2022

Abstract Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI)… More >

Somenath Bera¹, Vimal K. Shrivastava², Suresh Chandra Satapathy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 219-250, 2022, DOI:10.32604/cmes.2022.020601 - 21 July 2022

Abstract Hyperspectral image (HSI) classification has been one of the most important tasks in the remote sensing community over the last few decades. Due to the presence of highly correlated bands and limited training samples in HSI, discriminative feature extraction was challenging for traditional machine learning methods. Recently, deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification. Among various deep learning models, convolutional neural networks (CNNs) have shown huge success and offered great potential to yield high performance in HSI classification. Motivated… More >

Kriti Mahajan¹, Urvashi Garg¹, Nitin Mittal², Yunyoung Nam³, Byeong-Gwon Kang^4,*, Mohamed Abouhawwash^5,6

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3705-3720, 2022, DOI:10.32604/cmc.2022.027936 - 16 June 2022

Abstract Spectral unmixing is essential for exploitation of remotely sensed data of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members and their matching fractional, draft rules abundances for every pixel in HSI. This paper aims to unmix hyperspectral data using the minimal volume method of elementary scrutiny. Moreover, the problem of optimization is solved by the implementation of the sequence of small problems that are constrained quadratically. The hard constraint in the final step for the abundance fraction is then replaced with… More >

Kriti¹, Mohd Anul Haq², Urvashi Garg¹, Mohd Abdul Rahim Khan^2,*, V. Rajinikanth³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 939-957, 2022, DOI:10.32604/cmc.2022.023169 - 24 February 2022

Abstract A crucial task in hyperspectral image (HSI) taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube. For classification of images, 3-D data is adjudged in the phases of pre-cataloging, an assortment of a sample, classifiers, post-cataloging, and accurateness estimation. Lastly, a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken. In topical years, sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands. Encouraged by those efficacious solicitations, sparse representation… More >

Lihan Cui¹, Lijie Yan¹, Xiaohu Zhao¹, Lin Yuan², Jing Jin³, Jingcheng Zhang^1,*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 621-634, 2021, DOI:10.32604/phyton.2021.015511 - 07 February 2021

Abstract Tea plant stresses threaten the quality of tea seriously. The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation. In recent years, hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases, pests and some other stresses at the leaf level. However, the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale. In this study, based on the canopy-level hyperspectral imaging data, the methods for identifying and differentiating the three commonly occurred tea… More >

J. Banumathi¹, A. Muthumari², S. Dhanasekaran³, S. Rajasekaran⁴, Irina V. Pustokhina⁵, Denis A. Pustokhin⁶, K. Shankar^7,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2393-2407, 2021, DOI:10.32604/cmc.2021.015605 - 05 February 2021

Abstract Due to the advancements in remote sensing technologies, the generation of hyperspectral imagery (HSI) gets significantly increased. Accurate classification of HSI becomes a critical process in the domain of hyperspectral data analysis. The massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground materials. Presently, deep learning (DL) models particularly, convolutional neural networks (CNNs) become useful for HSI classification owing to the effective feature representation and high performance. In this view, this paper introduces a new DL based Xception model for HSI analysis and classification,… More >