Dual-Model Collaboration Consistency Semi-Supervised Learning for Few-Shot Lithology Interpretation

Abstract

Geological environment remote sensing (GERS) interpretation contributes to lithological mapping, disaster prediction, soil erosion monitoring, and so on. However, the rich diversity, complex distribution, interclass similarities, and uncertainties in data quality of geological elements pose challenges to GERS interpretation. In addition, current automatic feature extraction of GERS elements, which rely on deep learning (DL) and remote sensing (RS) information process technologies, often require sufficient labeled data. Due to the enormous labor cost and specialized expertise needed, labeled GERS samples are limited to training the data-driven models. To tackle the above challenges, we introduce the semi-supervised dual-model progressive self-training (DM-ProST) framework. This framework employs two DL networks with different initializations as evaluator models to correct each other. A sample filtering strategy is then implemented to evaluate the quality of unlabeled samples, selecting high-quality and reliable ones to expand the training set. In addition, a fully connected conditional random field (CRF) module is incorporated to optimize DL network prediction maps, thereby enhancing the boundary performance of segmentation results. The framework utilizes a multitask loss function that combines consistency loss with cross-entropy, enabling the models to learn discriminative GERS features. This process accurately generates pseudo-labels and achieves precise lithology mapping of GERS with a small amount of annotation samples. Finally, we conducted an experimental evaluation on the Landsat 8 dataset in Xinjiang, China, and massive experiments proved the effectiveness of DM-ProST.