Hyperspectral images （HSIs） capture reflectance values across hundreds of contiguous spectral bands at each spatial location，thus facilitating the precise discrimination of materials with subtle spectral differences by generating rich spectral-spatial information. This high spectral resolution makes HSIs particularly valuable for many application，such as environmental monitoring，land cover mapping，precision agriculture，and change detection （CD） applications. CD in multitemporal HSIs aims to identify meaningful surface alterations by comparing images of the same scene acquired at different times. Nowadays，accurate HSI-CD plays a critical role in ecological monitoring，urban expansion analysis，and disaster assessment. However，this task faces significant challenges，including high dimensionality，spectral redundancy，spatial heterogeneity，and the difficulty of effectively integrating spatial，spectral，and temporal features. Traditional methods like change vector analysis （CVA），principal component analysis （PCA），and multivariate alteration detection （MAD），rely on hand-crafted features and empirical thresholding. Despite being computationally efficient，these approaches are sensitive to noise，illumination variations，and atmospheric effects. Deep learning methods，particularly convolutional neural networks （CNNs），have demonstrated superior performance by automatically learning hierarchical features. While most CNN-based frameworks adopt dual-branch Siamese architectures，they still struggle to capture long-range dependencies and explicit temporal dynamics.

To address the abovementioned limitations，a novel time-difference-guided network （TDG-Net） is introduced in this paper. The proposed method employs a Siamese architecture with Vision Mamba as the feature extraction backbone to efficiently capture long-range spatial dependencies with linear computational complexity. Two core components are integrated： the time storage module （TSM） and the temporal difference guidance strategy. Bitemporal HSIs are first fed into the dual-branch Vision Mamba backbone to extract multilevel spatial-spectral features. Then，the TSM performs sequential temporal modeling on these features，using a simplified long short-term memory （sLSTM）. To reduce computational cost，spectral compression via 1 × 1 convolution and spatial down-sampling via max pooling are applied before feeding features into the sLSTM. The sLSTM generates explicit temporal difference representations. The temporal difference guidance strategy then converts these low-resolution difference features into spatial attention weights through bilinear up-sampling and ReLU activation. These weights are then fed back to the dual-branch network via residual connections，adaptively emphasizing change-relevant regions and suppressing unchanged areas at each hierarchical level. Finally，to produce the binary change detection map，the enhanced multilevel features are fused and passed to the classification head. To mitigate severe class imbalance，Dice loss is employed instead of conventional cross-entropy loss，directly optimizing the overlap between predicted and ground-truth change regions. Compared with recent Mamba-based methods，the core distinction of TDG-Net lies in its explicit modeling and hierarchical guidance of temporal differences across multiple feature levels via a lightweight TSM and residual feedback，rather than relying primarily on high-level implicit fusion.

Comprehensive experiments are conducted on three widely used benchmark hyperspectral datasets： River，Farmland，and Hermiston. All experiments are implemented on an NVIDIA RTX 3090 GPU using TensorFlow-GPU 2.5.0. Performance is then evaluated using overall accuracy （OA） and Kappa coefficient，with results averaged over 10 independent runs. Ablation studies confirm the significant contributions of the TSM and temporal difference guidance strategy. Comparative experiments against state-of-the-art methods demonstrate that TDG-Net consistently outperforms all competitors. On the River dataset，TDG-Net achieves an OA of 96.54% and a Kappa of 78.51%. On the Hermiston dataset，it reaches an OA of 98.21% and a Kappa of 91.79%. On the Farmland dataset，it attains an OA of 95.87% and a Kappa of 90.11%. Additional analyses on model complexity show that TDG-Net maintains competitive parameter count，floating point operations （FLOPs），and inference time while achieving superior accuracy.

The proposed TDG-Net effectively addresses the key limitations of existing HIS CD methods by explicitly modeling temporal differences at multiple hierarchical levels and guiding feature learning with adaptive attention. Furthermore，by integrating a lightweight TSM based on simplified LSTM and a temporal difference guidance strategy within a Vision Mamba backbone，the method captures rich spatial-spectral-temporal dynamics while effectively suppressing pseudo-changes. The introduction of Dice loss further alleviates the severe class imbalance problem. Extensive experiments on three benchmark datasets demonstrate that TDG-Net achieves state-of-the-art performance in terms of quantitative metrics and visual quality，exhibiting strong robustness and generalization in complex scenarios. Additionally，compared with existing Mamba-based methods，such as SAVDGN and CDMamba，which primarily rely on implicit high-level fusion，TDG-Net explicitly models and hierarchically guides temporal differences across multiple feature levels through a lightweight TSM and residual feedback mechanism，thus achieving more fine-grained change representation.