Leveraging Hyperspectral Remote Sensing Imaging for Agricultural Crop Classification Using Coot Bird Optimization With Entropy-Based Feature Fusion Model

Abstract

Hyperspectral remote sensing (HSRS) or imaging spectroscopy is a novel method for obtaining a spectrum in every place of a massive array of spatial locations towards many spectral wavelengths that can be employed to make coherent images. HSRS comprises acquisition of digital images in many narrow, contiguous spectral bands through the visible, Near Infrared (NIR), Thermal Infrared (TIR), and Mid-Infrared (MIR) regions of electromagnetic spectrum. For the application of agricultural areas, remote sensing methods can be examined and implemented to the advantage of continuous and quantitative monitoring. In particular, hyperspectral images (HSI) are assumed to be accurate for agriculture as they provide physical and chemical data on vegetation. This study presents a new Agricultural Crop Classification using Coot Bird Optimization with Entropy-based Feature Fusion (ACC-CBOEFF) technique on HSI. The presented ACC-CBOEFF technique exploits the feature fusion and parameter tuning concepts to determine different types of crops on the HSI. Primarily, the ACC-CBOEFF technique exploits Gabor filtering as a preprocessing step. The entropy-based feature fusion process comprises ShuffleNet, EfficientNet, and DenseNet models for feature extraction. Moreover, Deep Variational Autoencoder (DVAE) system was utilized for crop type classification process. Furthermore, the CBO technique can be employed for the optimal hyper-parameter tuning of the DVAE approach. The simulation outcomes of the ACC-CBOEFF technique are tested on benchmark dataset, and the extensive outcomes highlighted the performance of the ACC-CBOEFF system over other recent state of art approaches.

Keywords

• Remote sensing
• deep learning
• coot bird optimization
• entropy-based fusion process
• agricultural crop classification