  Hyperspectral image (HSI) classification is pivotal in the remote sensing
(RS) field, particularly with the advancement of deep learning techniques.
Sequential models, adapted from the natural language processing (NLP) field
such as Recurrent Neural Networks (RNNs) and Transformers, have been tailored
to this task, offering a unique viewpoint. However, several challenges persist
1) RNNs struggle with centric feature aggregation and are sensitive to
interfering pixels, 2) Transformers require significant computational resources
and often underperform with limited HSI training samples, and 3) Current
scanning methods for converting images into sequence-data are simplistic and
inefficient. In response, this study introduces the innovative Mamba-in-Mamba
(MiM) architecture for HSI classification, the first attempt of deploying State
Space Model (SSM) in this task. The MiM model includes 1) A novel centralized
Mamba-Cross-Scan (MCS) mechanism for transforming images into sequence-data, 2)
A Tokenized Mamba (T-Mamba) encoder that incorporates a Gaussian Decay Mask
(GDM), a Semantic Token Learner (STL), and a Semantic Token Fuser (STF) for
enhanced feature generation and concentration, and 3) A Weighted MCS Fusion
(WMF) module coupled with a Multi-Scale Loss Design to improve decoding
efficiency. Experimental results from three public HSI datasets with fixed and
disjoint training-testing samples demonstrate that our method outperforms
existing baselines and state-of-the-art approaches, highlighting its efficacy
and potential in HSI applications.
