# Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification

arXiv: https://arxiv.org/abs/2508.02839 · HTML: https://mlsanity.com/paper/2508.02839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Efficient Content-Based Sparse Attention with Routing Transformers](https://mlsanity.com/paper/2003.05997/) (2020)
- [Vision Transformer with Deformable Attention](https://mlsanity.com/paper/2201.00520/) (2022)
- [Exphormer: Sparse Transformers for Graphs](https://mlsanity.com/paper/2303.06147/) (2023)
- [Adaptively Sparse Transformers](https://mlsanity.com/paper/1909.00015/) (2019)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [Sparse is Enough in Scaling Transformers](https://mlsanity.com/paper/2111.12763/) (2021)
- [Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series](https://mlsanity.com/paper/1811.10166/) (2018)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks](https://mlsanity.com/paper/1704.04055/) (2017)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12487/) (2024)
- [SparseSwin: Swin Transformer with Sparse Transformer Block](https://mlsanity.com/paper/2309.05224/) (2023)

## Abstract (arXiv)

Although MODIS time series data are critical for supporting dynamic, large-scale land cover land use classification, it is a challenging task to capture the subtle class signature information due to key MODIS difficulties, e.g., high temporal dimensionality, mixed pixels, and spatial-temporal-spectral coupling effect. This paper presents a novel spatial-temporal-spectral Mamba (STSMamba) with deformable token sequence for enhanced MODIS time series classification, with the following key contributions. First, to disentangle temporal-spectral feature coupling, a temporal grouped stem (TGS) module is designed for initial feature learning. Second, to improve Mamba modeling efficiency and accuracy, a sparse, deformable Mamba sequencing (SDMS) approach is designed, which can reduce the potential information redundancy in Mamba sequence and improve the adaptability and learnability of the Mamba sequencing. Third, based on SDMS, to improve feature learning, a novel spatial-temporal-spectral Mamba architecture is designed, leading to three modules, i.e., a sparse deformable spatial Mamba module (SDSpaM), a sparse deformable spectral Mamba module (SDSpeM), and a sparse deformable temporal Mamba module (SDTM) to explicitly learn key information sources in MODIS. The proposed approach is tested on MODIS time series data in comparison with many state-of-the-art approaches, and the results demonstrate that the proposed approach can achieve higher classification accuracy with reduced computational complexity.