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  • Open Access


    A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

    Usman Khan1, Muhammad Khalid Khan1, Muhammad Ayub Latif1, Muhammad Naveed1,2,*, Muhammad Mansoor Alam2,3,4, Salman A. Khan1, Mazliham Mohd Su’ud2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

    Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

  • Open Access


    A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

    M. R. Vimala Devi, S. Kalaivani*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2459-2476, 2023, DOI:10.32604/iasc.2023.038183

    Abstract Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images. Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination, atmospheric, and environmental conditions. Here, endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions. Accordingly, a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images. The divide and conquer method is applied as the first… More >

  • Open Access


    Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

    Sultan Alahmari1, Saud Yonbawi2, Suneetha Racharla3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI:10.32604/csse.2023.036362

    Abstract Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs… More >

  • Open Access


    Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

    Imran Ali1, Zohaib Mushtaq2, Saad Arif3, Abeer D. Algarni4,*, Naglaa F. Soliman4, Walid El-Shafai5,6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 303-319, 2023, DOI:10.32604/csse.2023.034374

    Abstract Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral… More >

  • Open Access


    Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

    Mesfer Al Duhayyim1,*, 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

    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 precise for agriculture as they… More >

  • Open Access


    Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

    S. Prasanna Bharathi1,2, S. Srinivasan1,*, G. Chamundeeswari1, B. Ramesh1

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

    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 consuming, inconsistent prediction etc. To… More >

  • Open Access


    Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

    B. Chellapraba1,*, 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

    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) classification (RDADL-HIC) technique. The proposed… More >

  • Open Access


    Non-Negative Minimum Volume Factorization (NMVF) for Hyperspectral Images (HSI) Unmixing: A Hybrid Approach

    Kriti Mahajan1, Urvashi Garg1, Nitin Mittal2, Yunyoung Nam3, Byeong-Gwon Kang4,*, Mohamed Abouhawwash5,6

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

    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 a loss function of hinge… More >

  • Open Access


    Fusion-Based Deep Learning Model for Hyperspectral Images Classification

    Kriti1, Mohd Anul Haq2, Urvashi Garg1, Mohd Abdul Rahim Khan2,*, V. Rajinikanth3

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

    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 (SR) has likewise been presented… More >

  • Open Access


    A Noise-Resistant Superpixel Segmentation Algorithm for Hyperspectral Images

    Peng Fu1,2, Qianqian Xu1, Jieyu Zhang3, Leilei Geng4,*

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 509-515, 2019, DOI:10.32604/cmc.2019.05250

    Abstract The superpixel segmentation has been widely applied in many computer vision and image process applications. In recent years, amount of superpixel segmentation algorithms have been proposed. However, most of the current algorithms are designed for natural images with little noise corrupted. In order to apply the superpixel algorithms to hyperspectral images which are always seriously polluted by noise, we propose a noise-resistant superpixel segmentation (NRSS) algorithm in this paper. In the proposed NRSS, the spectral signatures are first transformed into frequency domain to enhance the noise robustness; then the two widely spectral similarity measures-spectral angle mapper (SAM) and spectral information… More >

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