Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm

Mustafa Altaee¹, Talib A.², M. A.  Jalil³, Ali J.^4,5, Thamer A. Alalwani⁶

¹ Department of medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq;

²Computer Communications Engineering Department, National University of science and technology , Thi Qar, Iraq

³Department of Computer Engineering techniques, Alturath University college, Baghdad, Iraq 

⁴Department of Computer Engineering techniques, Mazaya University college, Thi Qar, Iraq

⁵ MEU Research Unit, Middle East University, Amman 11831, Jordan.

⁶Radiological Techniques Department, Al- Mustaqbal University College, 51001 Hilla, Iraq.

Emails: m.altaee@alfarahidiuc.edu.iq; ali.j@nust.edu.iq; mohammed.jalil@turath.edu.iq; A.jawad@mpu.edu.iq; thamerabdalhamza@uomus.edu.iq

Abstract

The collection of fetures in both multispectral and hyperspectral domains is possible with Hyperspectral Image (HSI). It comprises a vast array of multispectral bands with functional relationships. However, they become more complex when dealing with small samples. To tackle this issue, researchers employed a deep learning convolutionary neural network system (DL-CNN) and implemented a temporal abstraction strategy to grade HSI. This approach is an intelligent multi-level feature fusion that combines the temporal abstraction strategy and DL-CNN for HSI grading. Researchers have introduced the impact of seven separate classifiers in implementing the Location estimation on our broad CNN framework, which plays the shallow CNN model's main training phase. PSO, Adagrad, Plans to implement, Alexnet, Adam, Environmental benefits, and Nadam are the seven distinct remained significantly included in this analysis. A detailed study of the four multispectral remote sensing techniques sets showed the deep CNN system's supremacy followed with the HSI identification AlexNet Optimizer.

Keywords: Convolutional Neural Network; Image classification; Intelligent Multi-Level Feature Fusion;  Remote sensing; deep learning.