Enhancing hyperspectral remote sensing image classification using robust learning technique
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Advanced sensor tech integrates into diverse applications, including remote sensing, robotics, and IoT. Combining artificial intelligence (AI) with sensors enhances their capabilities, creating smart sensors, revolutionizing remote sensing and Internet of Things (IoT). This synergy forms a potent technology in the field. This study carries out a comprehensive analysis of the progress made in Hyperspectral sensors and AI-based classification techniques that are employed in remote sensing fields that utilize hyperspectral images. The classification of images obtained from Hyperspectral Sensors (HSS) has emerged as a prominent research subject within the domain of remote sensing. HSS offer a wealth of information across numerous spectral bands, supporting diverse applications such as land cover classification, environmental monitoring, agricultural assessment, change detection, and more. However, the abundance of data present in HSS also poses the challenge called the curse of dimensionality. The reduction of data dimensionality is crucial before applying any machine learning model to achieve optimal results. The present study introduces a new hybrid strategy combining the Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and principal component analysis (PCA) for the purpose of classifying hyperspectral images. PCA is first applied to obtain an optimal set of discriminative features by eliminating highly correlated and redundant features. These features are then fed into the BPVAM model for classification. The addition of the momentum term in the weight update equation of the backpropagation algorithm helped achieve faster convergence with high accuracy. The proposed model was subjected to evaluation through experiments conducted on two benchmark datasets. These results indicated that the hybrid model based on BPVAM with PCA is an efficient technique for HSS classification.
Açıklama
Anahtar Kelimeler
Backpropagation With Variable Adaptive Momentum, Principal Component Analysis, Hyperspectral Image Classification, Dimensionality Reduction, Hyperspectral Sensors
Kaynak
Journal of King Saud University Science
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
36
Sayı
1
