The global average pooling approach used in the 3D-ConvSST architecture may have limitations in capturing intricate spatial details and fine-grained features, especially in complex scenes with overlapping classes or subtle distinctions. One potential limitation is the loss of spatial information during pooling, which could impact the model's ability to differentiate between classes with similar spectral signatures but distinct spatial patterns.
To address these limitations, alternative pooling or attention mechanisms can be explored to enhance feature representation. One approach could be to implement spatial pyramid pooling to capture multi-scale spatial information and preserve detailed spatial context. Another option is to incorporate self-attention mechanisms, such as spatial self-attention or channel-wise attention, to selectively focus on relevant spatial regions and features.
Furthermore, adaptive pooling strategies, like dynamic pooling based on feature importance or spatial relevance, can be implemented to adjust pooling operations based on the characteristics of the input data. By exploring these alternative pooling and attention mechanisms, the feature representation in the 3D-ConvSST architecture can be further improved to handle complex remote sensing tasks more effectively.