Transferring RGB-Pretrained CNNs to Multispectral UAV Imagery for Salt Marsh Vegetation Classification

Accurate classification of salt marsh vegetation is crucial for coastal wetland monitoring, but fine-grained species discrimination remains difficult, particularly when only limited training data are available for deep learning approaches. To address this challenge, this paper presents a transfer learning-based framework for classifying salt marsh vegetation using UAV multispectral imagery, focusing on a seven-class taxonomy representative of dominant species and water surfaces. Multispectral orthophotos acquired with a MicaSense Dual-Camera system (10 spectral bands) are combined with five vegetation indices to create rich multi-channel inputs. A classification architecture inspired by heterogeneous transfer learning is developed, where a feature-encoding branch compresses the 15-channel input into three channels before processing through a VGG-16 Convolutional Neural Network (CNN), pre-trained on RGB imagery. By leveraging transfer learning from VGG-16, the proposed model achieves high classification accuracy even with limited training data. Performance is compared with traditional machine learning classifiers, namely Support Vector Machines (SVMs) and Random Forest (RF). Results show that the deep learning approach significantly outperforms SVM and RF, achieving an overall accuracy of 98.4% when jointly using spectral bands and vegetation indices. These findings demonstrate the potential of integrating multispectral UAV data and CNN-based classification to support accurate mapping of heterogeneous salt marsh communities for ecological monitoring and coastal management.