Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data.

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 encapsulate a wealth of spectral and spatial information, enabling detailed analyses of the Earth's surface features. However, the complexity of these 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 tested on the EuroSAT and LCZ42 datasets and (2) a species-level identification of planktic foraminifera. Multichannel images are fed into an ensemble of Convolutional Neural Networks (CNNs) (ResNet50, MobileNetV2, and DenseNet201), where each network is trained using three channels obtained from the multichannel images, and two custom networks (one based on ResNet50 and the other one based on attention) where the input is a multiband image. The ensemble learning framework harnesses these variations to improve classification accuracy, surpassing other advanced methods. The proposed system, implemented in MATLAB 2024b and PyTorch 2.6, is shown to achieve higher classification accuracy than those of human experts for species-level identification of planktic foraminifera (>92% vs. 83%) and state-of-the-art performance on the tested planktic foraminifera, the EuroSAT and LCZ42 datasets.