Water Quality Classification for Inland Lakes and Ponds with Few Color Image Samples Based on Triple-GAN and CSNN

Abstract

Water color is an important representation reflecting the characteristics of its quality in inland lakes or ponds; however, sufficient water color image samples are often difficult to obtain due to the limitation of fishery production. For few color image samples, the existing data enhancement methods based on the depth generation model have the problems of low quality of generated data, difficulty of network training, and so on; moreover, for image classification, traditional methods based on convolutional neural network (CNN) cannot effectively extract the potential manifold structure features in the image and the full connection layer in CNN cannot simulate biological neurons well, resulting in high time cost and low efficiency. In this paper, a water quality classification method has been proposed to solve the above problems, the improved semisupervised triple-generation adversarial network (triple-GAN) algorithm is used to enhance the few water color image samples, and the feature data can then be extracted from enhanced data by manifold learning method t-distributed stochastic neighborhood embedding (t-SNE). Moreover, convolutional spiking neural network (CSNN), in which spiking neural network (SNN) has replaced the original full connection layer of CNN, is used for final water quality classification. The main contribution of this paper is to build a new algorithm framework, introduce triple-GAN and CSNN into the field of classification of few water color image samples for the first time, and make an exploration of integrating artificial intelligence (AI) and water quality analysis problems. By comparing with traditional methods, the proposed method is proved to have the advantages of less time-consuming, low operation cost, and high classification accuracy.