The proposed 3D-ConvSST architecture can be extended to handle other remote sensing tasks beyond hyperspectral image classification by adapting its components and structure to suit the specific requirements of tasks like object detection or semantic segmentation.
For object detection, the architecture can be modified to include region proposal networks (RPNs) and anchor boxes to identify potential object locations. The 3D-Convolution guided Residual Module (CGRM) can be adjusted to focus on extracting features relevant to object boundaries and shapes. Additionally, the Transformer encoder can be enhanced to incorporate positional encodings and multi-scale feature fusion for better object localization.
In the case of semantic segmentation, the architecture can be tailored to output pixel-wise class labels by incorporating skip connections and decoder modules. The CGRM can be optimized to capture fine-grained spatial details, while the global average pooling can be replaced with spatial attention mechanisms to better preserve spatial information during feature aggregation.
By customizing the components and integrating task-specific modules, the 3D-ConvSST architecture can effectively address a broader range of remote sensing tasks beyond hyperspectral image classification.