Analysis and research of hyperspectral remote sensing image data based on CNN

The combination of artificial intelligence and hyperspectral remote sensing images can offer rich feature information and has great potential for application. In terms of water quality testing, traditional methods suffer from issues of insufficient st ability and coverage. Research on improving the design of convolutional neural networks, analyzing and extracting deep features from hyperspectral remote sensing images, and constructing an intelligent water quality monitoring model aimed at enhancing the ability of water quality monitoring. The study introduces the dual channel advantage of dual channel convolutional networks to extract multi-scale and global information from hyperspectral remote sensing images, and improves the global feature learning module and multi-scale feature fusion module in the dual channel to establish a water quality detection model that integrates the improved dual channel convolutional network and hyperspectral remote sensing images. The outcomes denoted that the overall accuracy of the water quality detection model reached 99.5%, the accuracy of dissolved oxygen detection was 97.5%, and the average accuracy of detecting other water quality indicators was 0.9, which was better than other water quality detection models. This indicates that the research model has higher accuracy in water quality detection. The research results are beneficial for accurate detection of water quality in a large area of water, and can reduce manual errors and detection costs.