Multiband Image Analysis Using Ensemble Neural Networks

The classification of multiband images captured by advanced sensors, such as satellite-mounted imaging systems, is a critical task in remote sensing and environmental monitoring. These sensors provide high-dimensional data that encapsulates a wealth of spectral and spatial information, enabling detailed analyses of the Earth’s surface features. However, the complexity of this data poses significant challenges for accurate and efficient classification. Our study describes and highlights methods for creating ensembles of neural networks for handling multiband images. Two applications are illustrated in this work: 1) satellite image classification trained on the EuroSAT dataset and 2) a species-level identification of planktic foraminifera. Multichannel images are fed into an ensemble of Convolutional Neural Networks (CNNs), where each network is trained using three channels, one each for the multichannel images. The ensemble learning framework harnesses these variations to improve classification accuracy, surpassing other advanced methods. The proposed system, implemented in MATLAB, is shown to achieve higher classification accuracy than those of human experts for species-level identification of planktic foraminifera and state-of-the-art performance on both the tested planktic foraminifera and the EuroSAT datasets.