Lithologic scene classification based on channel group fusion and adaptive feature filtering

Lithology classification is an important research direction in geological re-mote sensing. Lithology is a high-level semantic information and its features are easily masked by vegetation, posing challenges in remote sensing feature extraction. In this study, we constructed a lithology scene classification da-taset named MSRS-LSC based on multi-source remote sensing data. Subse-quently, we proposed a lithology scene classification model called Channel Grouping Fusion and Adaptive Feature Filtering Network CGFAFFNet . This model consists of two modules: 1 Channel Grouping Fusion CGF module: group learning, channel-wise information mixing and interaction, and weighted fusion were performed to select key information on the feature maps that extracted at different depths by dense connection blocks 2 Adap-tive Feature Filtering module: Cascading the fusion features from different CGF modules and performing weighted calculations in both channel and spatial dimensions to further filter key feature information. The proposed model achieved an OA, F1 score, and Kappa of 80.99 ± 0.4 , 81.26 ± 0.38 , and 78.85 ± 0.44 , respectively, outperforming mainstream scene classification models.