S. Rajalakshmi^1,*, S. Nalini², Ahmed Alkhayyat³, Rami Q. Malik⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1673-1688, 2023, DOI:10.32604/csse.2023.034414 - 09 February 2023

Abstract Remote sensing image (RSI) classifier roles a vital play in earth observation technology utilizing Remote sensing (RS) data are extremely exploited from both military and civil fields. More recently, as novel DL approaches develop, techniques for RSI classifiers with DL have attained important breakthroughs, providing a new opportunity for the research and development of RSI classifiers. This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification (ISMOGCN-HRSC) model. The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs. In the presented ISMOGCN-HRSC More >

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 >

Yani Hou, Wenzhong Zhu, Erli Wang

Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 805-814, 2019, DOI:10.31209/2019.100000084

Abstract Hyperspectral remote sensing, with its narrow band imaging, provides the potential for fine identification of ground objects, and has unique advantages in mineral detection. However, the image is nonlinear and the pure pixel is scarce, so using standard spectrum detection will lead to an increase of the number of false alarm and missed detection. The density peak algorithm performs well in high-dimensional space and data clustering with irregular category shape. This paper used the density peak clustering to determine the cluster centers of various categories of images, and took it as the target spectrum, and More >