Hyperspectral Image Analysis for Water Quality Classification: A Hybrid Network Model Based on 3d Convolutional Neural Network and Capsule Network

Abstract

In recent years, the application of hyperspectral image (HSI) analysis technology has become prevalent in the field of classification, primarily focusing on land object classification. However, the application of water quality classification is relatively limited. Therefore, this study presents a hybrid network model for water quality classification, which combines the 3D convolutional neural network (3D-CNN) and capsule network (CapsNet). The 3D-CNN extracts features from the HSI data, and CapsNet learns a more abstract spatial representation. The proposed model effectively captures spatio-temporal features from HSI and accurately assesses water quality. To evaluate the performance of hybrid network model, spectral data of various water quality were collected and preprocessed using a UAV-carried spectrometer device. Rigorous classification experiments were conducted on the collected dataset, and the results of the other model experiments were compared with the hybrid network model. This comparison demonstrated the effectiveness of the proposed method. This advancement will enhance the reliability of decision support systems for managing and protecting water resources, and promote the sustainable utilization of water resources.

Keywords: Water quality classification, 3D convolutional neural network, Capsule network, Spectral feature, Hyperspectral image