Advancements in Hyperspectral Image Compression with LineRWKV

Hyperspectral Images are special types of pictures that capture information from many different wavelengths of light. These images are valuable for understanding our planet, helping with things like environmental monitoring, urban planning, and dealing with climate change. However, they take up a lot of space in terms of data storage and transmission. This makes Compression techniques very important to make sure we can send and store these images without losing too much information.

Traditionally, compressing these types of images onboard spacecraft has been complex and demanding. This is mainly because of the high amount of computing power needed and the lack of suitable training data for new compression methods. Recently, researchers have been looking at using Deep Learning techniques to improve how these images can be compressed. This article discusses a new method called LineRWKV, which focuses on compressing hyperspectral images effectively while using less computing power.

#The Need for Compression

Hyperspectral images are collected using advanced sensors on satellites. These sensors can capture a lot of detail by measuring light from many different wavelengths. However, this detail results in large amounts of data that can be challenging to handle. As technology improves and sensors capture more details, the amount of data increases even further. This means we must have effective methods to compress images so they can be sent to Earth, stored, and managed more easily.

Compression reduces the size of data without losing critical information. Even small improvements in compression can lead to significant savings in bandwidth, meaning that images can be transmitted faster and take up less space.

#Challenges of Onboard Compression

Compressing hyperspectral images onboard spacecraft isn't easy. The equipment on satellites often has limited processing capabilities and memory. Compressing large amounts of data needs methods that are efficient both in how they process data and in how they capture complex patterns within the images. Traditional methods, like the CCSDS-123.0-B-2 standard, have done well in this area, but researchers are always looking for better solutions.

Recent trends in deep learning have made it possible to approach image compression differently. However, applying deep learning to onboard compression is not straightforward. Traditional methods have worked effectively, utilizing simpler adaptive predictors learned from past data. In contrast, deep learning methods can struggle with adaptability and capturing the nonlinear aspects of image details.

#A New Approach: LineRWKV

In response to the challenges outlined, a new approach called LineRWKV has been developed. Unlike traditional methods, this new compression technique is based on Predictive Coding. This means it predicts the value of each pixel by looking at previous pixels. The difference between the predicted value and the actual pixel value is then compressed. This technique is beneficial because it allows for both lossless and lossy compression, which gives flexibility depending on the needs of the task.

LineRWKV has a unique architecture that recursively processes data line by line. This means it only uses the data needed for each specific line at a time, which helps reduce memory usage. It combines elements from two different types of neural networks: Transformers, known for their ability to capture complex relationships, and recurrent neural networks, which are good at handling sequential data.

#The Architecture of LineRWKV

The LineRWKV design includes several essential components:

1. Encoder: This part captures the relationship between different pixels and translates them into a feature space for further processing.

2. Line Predictor: This key component predicts the features of the next line based on the previous lines' features, allowing continuous operation.

3. Spectral Predictor: This component predicts the features of the next band in the image, helping to utilize the data across different wavelengths.

4. Decoder: This part converts the processed features back into the pixel values for the final output.

#Working of the Line Predictor

The line predictor is a significant feature of the LineRWKV model. It allows the system to process data without needing to store all past lines in memory. Instead, it focuses on a limited number of previous lines, making it possible to manage the computational load effectively.

The line predictor uses a specific type of neural network called RWKV, which balances the strengths of both transformers and recurrent networks. This allows it to adapt quickly to patterns in the images and maintain efficiency during processing.

#Working of the Spectral Predictor

Once the line predictor has processed the data, the spectral predictor comes into play. It looks at the difference between the predicted pixel values and the actual pixel values. By analyzing this difference, it can predict the next set of features in the spectral dimension. This part of the model is crucial for capturing the correlations that exist across different wavelengths in the hyperspectral images.

The decoder then takes the output from the spectral predictor and reconstructs the final pixel values. Instead of outputting a probability distribution for each pixel value, which can be cumbersome, the decoder focuses on predicting the pixel as a floating-point number that can be easily rounded to the nearest integer.

#Training and Inference

The LineRWKV model must be trained to achieve its best performance. During training, the model learns to predict pixel values accurately by minimizing the differences between the predicted and actual values. This training process ensures that the model can make accurate predictions when it is deployed onboard a satellite for real-time compression.

During inference, or actual use, the model operates efficiently. It processes one line at a time, keeping memory usage low while still maintaining high compression performance.

#Performance Evaluation

To evaluate the performance of LineRWKV, several experiments were conducted. These tests compared LineRWKV against established methods, including the CCSDS-123.0-B-2 standard, to analyze their compression efficiency.

#Results with the HySpecNet-11k Dataset

The primary dataset used for testing was HySpecNet-11k, which includes a large number of hyperspectral images collected from satellites. These images were divided into different patches for training and testing the model. The results showed that LineRWKV could outperform the CCSDS standard in terms of compression efficiency, especially at higher data rates.

#Comparison with Other Methods

In addition to CCSDS, the performance of LineRWKV was also compared with other state-of-the-art deep learning methods. These comparisons highlighted how traditional methods still maintain advantages in certain conditions while showing that LineRWKV offers strong performance, especially in high-rate scenarios.

#Transfer Learning

Another interesting aspect of LineRWKV is its ability to adapt to different satellite imagery through transfer learning. This means that a model trained on one type of hyperspectral data can be fine-tuned using data from a different satellite. This characteristic is particularly valuable since comprehensive datasets for new satellites may not be available before their launch.

After conducting tests on a different set of images from the PRISMA satellite, the model still performed well, especially after being fine-tuned with just a small number of additional images. This flexibility indicates that LineRWKV can be a practical solution for various satellite missions.

#Hardware Performance

Testing LineRWKV on low-power hardware demonstrated its efficiency for real-world applications. The design was tested on the Nvidia Jetson Orin Nano platform, which has limited processing capability. The model showed modest memory usage and high throughput during compression tasks. This capability is crucial for onboard applications, where resources are often restricted.

#Future Directions

While the current results are promising, there is always room for improvement. Future research could focus on optimizing the model even further, possibly looking into additional techniques that could enhance throughput while maintaining the quality of the compressed images. Exploring different architectures, adjustments to the training process, and testing further with real-world satellite data will help refine this model.

#Conclusion

The LineRWKV model presents a novel approach to compressing hyperspectral images onboard spacecraft. By combining predictive coding with an innovative neural network architecture, it demonstrates improved compression efficiency and reduced computational demands compared to traditional methods. As technology continues to evolve, advancements like LineRWKV will play a crucial role in the future of Earth observation and satellite imaging, making it easier to gather, store, and analyze vital data related to our planet.