This study examines the effectiveness of five deep learning models—ViTb-19, SwinV2-t, VGG-16, ResNet-50, and DenseNet-121—in distinguishing different vegetation types in the protected grasslands of Castilla y León region, Spain, following the guidelines of the Natura 92/43/CEE directive. Among the models, ResNet-50 achieved the highest weighted overall accuracy (OA) of 0.95, closely followed by SwinV2-t with an OA of 0.94, demonstrating their strong ability to detect complex patterns in satellite imagery. DenseNet-121 also performed competitively with a weighted OA of 0.93, while ViTb-19 and VGG-16 showed slightly lower performance. SwinV2-t, a transformer-based model, outperformed traditional CNN architectures in data-rich classes but faced challenges in classifying habitats with limited representation. Consequently, this study identifies these challenges that conventional transformer architectures pose in classifying certain habitats with limited representation and intricate features. Highlighting the advantages of deep learning technologies for environmental monitoring and conservation, the study provides important insights for adjusting neural network architectures for effective habitat classification. This suggests the necessity of selecting appropriate architectures such as SwinV2-t and ResNet50 to to effectively address the intricate requirements of satellite imagery analysis.

中文翻译：

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在 Sentinel-2 卫星影像数据上使用深度学习架构对受保护的草原栖息地进行分类


本研究根据 Natura 92/43/CEE 指令的指导方针，检查了五个深度学习模型（ViTb-19、SwinV2-t、VGG-16、ResNet-50 和 DenseNet-121）在区分西班牙卡斯蒂利亚-莱昂地区受保护草原中不同植被类型的有效性。在这些模型中，ResNet-50 实现了最高的加权总体精度 （OA） 0.95，紧随其后的是 SwinV2-t，OA 为 0.94，展示了它们检测卫星图像中复杂模式的强大能力。DenseNet-121 的表现也具有竞争力，加权 OA 为 0.93，而 ViTb-19 和 VGG-16 的性能略低。SwinV2-t 是一种基于 transformer 的模型，在数据丰富的类别中优于传统的 CNN 架构，但在对代表性有限的栖息地进行分类时面临挑战。因此，本研究确定了传统变压器架构在对某些代表性有限且特征复杂的栖息地进行分类时带来的这些挑战。该研究强调了深度学习技术在环境监测和保护方面的优势，为调整神经网络架构以实现有效的栖息地分类提供了重要的见解。这表明有必要选择合适的架构，例如 SwinV2-t 和 ResNet50，以有效解决卫星图像分析的复杂要求。