Hyperspectral image (HSI) classification remains a challenging task due to the high spectral dimensionality and the need for effective spatial feature integration. To address this, we propose a lightweight yet effective deep learning architecture named Patchwise Spectral-Spatial MambaNet (PatchMamba) that jointly models local spatial context and global spectral dependencies. The framework first extracts fixed-size local patches from the input hyperspectral cube and encodes spatial features using two-dimensional convolutional layers. These representations are reshaped into token sequences and passed through a stack of Spectral-Spatial Mamba (SS-Mamba) blocks, each composed of dense layers, layer normalization, and residual connections. A global average pooling layer aggregates the refined token features, and a final softmax classifier produces the predicted land-cover labels. The model was evaluated on the widely used Pavia University dataset and demonstrated superior performance over baseline models, including a fully connected deep neural network (FC-DNN) and a non-patch-based SS-Mamba architecture. PatchMamba achieved an overall accuracy of 96.4%, with strong per-class consistency, reduced confusion in spectrally similar classes, and high spatial coherence in the resulting classification maps. Both quantitative and qualitative results confirm the robustness and efficiency of the proposed method, making it a competitive solution for real-world HSI classification tasks.