An Innovative Approach: Sea Ice Types Classification Using Convolutional Neural Networks with DDDTDWT Filter

• Published in: International Journal of Soft Computing and Engineering
• Main Author: Dr. Venkata Kondareddy Gajjala
• Other Authors: Dr. T.J. Naga Lakshmi, Professor
• Format: Article in Journal/Newspaper
• Language: English
• Published: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) 2024
• Subjects: Advanced Microwave Scanning Radiometer - Earth Observing System Sensor (AMSR-E); Convolutional Neural Network (CNN); Arctic; Antarctic; Antarc*; Climate change; Sea ice
• Online Access: https://doi.org/10.35940/ijsce.B8099.14010324

Abstract: Studying sea ice and its interaction with climate change is crucial due to its significant impact on the environment, society, and global stability. The pressing need to address the underlying reasons for the rapid melting of Arctic and Antarctic sea ice is underscored by its adverse effects on the environment and society. In this proposed study, a Convolutional Neural Network (CNN) is utilized to predict ice types using data from the NSIDC DAAC Advanced Microwave Scanning Radiometer - Earth Observing System Sensor (AMSR-E) collection. This dataset contains parameters such as sea ice types and spans data products from June 2002, obtained from the NASA Data Centre. By employing hand-crafted features as input and a single layer of hidden nodes, the CNN used in this approach generates improved estimates of ice types, outperforming traditional image analysis methods. At each stage, ConvNets use diverse filter banks, feature extraction pooling layers, and fully connected layers with basic activation functions like Relu. This allows the network to build multifaceted hierarchies of features. The sea ice type estimates produced by the CNN are then compared with those obtained from passive microwave brightness temperature data using existing algorithms as well as a proposed CNN algorithm, resulting in an increased classification accuracy of 98.58%. This improvement is particularly notable in the reduction of the error rate, which has been effectively minimized from 3.01% without feature selection to 1.42% with infinite feature selection. When compared to existing algorithms, the CNN demonstrates superior performance. These findings underscore the impact of input patch size, CNN layer count, and input size on the model's performance.