Samba: A State Space Model-based Semantic Segmentation Framework for High-Resolution Remotely Sensed Images

Centrala begrepp

Samba, a novel semantic segmentation framework built on the Mamba architecture, effectively captures global semantic information in high-resolution remotely sensed images with low computational complexity, outperforming state-of-the-art CNN and ViT-based methods.

Sammanfattning

The article introduces Samba, a semantic segmentation framework for high-resolution remotely sensed images, which is built upon the Mamba architecture. The key highlights are:

1. Limitations of existing methods:

   • CNN-based methods struggle with the limited receptive field when handling high-resolution images.
   • ViT-based methods face challenges in dealing with long sequences and require large amounts of training data.

2. Samba architecture:

   • Samba utilizes an encoder-decoder architecture, with Samba blocks as the encoder and UperNet as the decoder.
   • The Samba block replaces the multi-head self-attention in ViT with a Mamba block, which efficiently captures global semantic information using a State Space Model (SSM).
   • The combination of the Mamba block and MLP enhances the model's representational capacity and learning ability for complex data.

3. Experiments and results:

   • Samba is evaluated on the LoveDA dataset, a high-resolution remotely sensed imagery dataset.
   • Samba outperforms top-performing CNN-based (ConvNeXt, ResNet50) and ViT-based (Swin-T) methods in terms of segmentation accuracy (mIoU) without using pre-trained parameters.
   • Samba achieves a new benchmark in performance for Mamba-based techniques in semantic segmentation of remotely sensed images.

4. Potential future directions:

   • Combining Mamba with CNNs to enhance the capability of capturing local details.
   • Exploring efficient and effective transfer learning methods tailored to the Mamba architecture.
   • Applying Mamba-based methods to semantic segmentation of multi-channel data, such as hyperspectral imagery.

Statistik

The article does not provide any specific numerical data or statistics. However, it presents the following key figures:

Figure 1(a) illustrates the limited receptive field of CNN, which becomes 7×7 after two 3×3 convolutions.
Figure 1(b) shows how ViT slices an image into patches and performs multi-head self-attention to possess a global receptive field.
Table 1 summarizes the training settings for the compared semantic segmentation networks, including decoder, encoder, image size, total training iterations, batch size, optimizer, initial learning rate, warmup iterations, learning rate schedule, weight decay, loss function, and data augmentation.
Table 2 presents the performance comparison of Samba and other methods on the LoveDA dataset, including mIoU, flops per patch, and number of parameters.

Citat

The article does not contain any direct quotes that are particularly striking or support the key logics.

Viktiga insikter från

Samba

by Qinfeng Zhu,... på arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01705.pdf

Djupare frågor

How can the Samba framework be further improved to better capture local details while maintaining its strength in global semantic understanding

To enhance the Samba framework's ability to capture local details while maintaining its proficiency in global semantic understanding, a hybrid approach can be considered. By integrating Mamba with CNNs, the model can benefit from the local feature extraction capabilities of CNNs while leveraging Mamba's strength in capturing global semantic information efficiently. This hybrid architecture would allow for a more comprehensive analysis of the image data, combining the detailed local context provided by CNNs with the holistic understanding offered by Mamba. Additionally, incorporating attention mechanisms within the Samba blocks can help focus on relevant local regions while considering the broader context, striking a balance between local and global information. Fine-tuning the Mamba block's parameters to adapt to different scales of features can also improve the model's ability to capture intricate details without compromising its global semantic understanding.

What are the potential challenges and limitations of applying Mamba-based methods to other types of remote sensing data, such as hyperspectral or LiDAR data

Applying Mamba-based methods to other types of remote sensing data, such as hyperspectral or LiDAR data, may pose several challenges and limitations. One primary challenge is the inherent differences in data characteristics between optical imagery and hyperspectral or LiDAR data. Hyperspectral data, for instance, contains information across numerous spectral bands, requiring specialized processing techniques to extract meaningful features. Mamba's linear state space model may need to be adapted to effectively handle the multi-dimensional nature of hyperspectral data. Similarly, LiDAR data, which provides detailed 3D information, may require modifications to the Mamba architecture to incorporate spatial relationships in addition to spectral information. Furthermore, the computational complexity of Mamba-based methods may increase when dealing with multi-channel data, necessitating optimization strategies to maintain efficiency while processing hyperspectral or LiDAR data.

How can the Samba framework be adapted or extended to address other computer vision tasks beyond semantic segmentation, such as object detection or instance segmentation in remotely sensed imagery

To adapt the Samba framework for other computer vision tasks beyond semantic segmentation in remotely sensed imagery, such as object detection or instance segmentation, several modifications and extensions can be considered. For object detection, the Samba framework can be augmented with region proposal networks (RPNs) to identify object bounding boxes in the image. By integrating RPNs with the Samba encoder-decoder architecture, the model can effectively localize and classify objects within the scene. Additionally, for instance segmentation, the Samba framework can be extended to predict pixel-wise object masks by incorporating instance-specific information into the decoding process. Utilizing instance-aware features and refining the segmentation masks based on object boundaries can enhance the model's ability to differentiate between individual instances within the image. By adapting the Samba framework with task-specific modules and loss functions tailored to object detection and instance segmentation, it can be effectively applied to a broader range of computer vision tasks in remotely sensed imagery.