Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging

TL;DR

This paper introduces Block Modulated Imaging (BMI), a novel on-orbit remote sensing image compression method that achieves high-resolution, fast acquisition with low computational complexity, suitable for satellite deployment.

Contribution

The paper proposes BMI, a new compressed sensing approach that enables rapid, high-resolution on-orbit image compression without complex hardware, and introduces a specialized decoding network for superior reconstruction.

Findings

BMI significantly improves imaging speed and resolution.

The decoding network outperforms existing methods in reconstruction quality.

Prototype testing shows practical viability for satellite applications.

Abstract

The growing field of remote sensing faces a challenge: the ever-increasing size and volume of imagery data are exceeding the storage and transmission capabilities of satellite platforms. Efficient compression of remote sensing imagery is a critical solution to alleviate these burdens on satellites. However, existing compression methods are often too computationally expensive for satellites. With the continued advancement of compressed sensing theory, single-pixel imaging emerges as a powerful tool that brings new possibilities for on-orbit image compression. However, it still suffers from prolonged imaging times and the inability to perform high-resolution imaging, hindering its practical application. This paper advances the study of compressed sensing in remote sensing image compression, proposing Block Modulated Imaging (BMI). By requiring only a single exposure, BMI significantly enhances imaging acquisition speeds. Additionally, BMI obviates the need for digital micromirror devices and surpasses limitations in image resolution. Furthermore, we propose a novel decoding network specifically designed to reconstruct images compressed under the BMI framework. Leveraging the gated 3D convolutions and promoting efficient information flow across stages through a Two-Way Cross-Attention module, our decoding network exhibits demonstrably superior reconstruction performance. Extensive experiments conducted on multiple renowned remote sensing datasets unequivocally demonstrate the efficacy of our proposed method. To further validate its practical applicability, we developed and tested a prototype of the BMI-based camera, which has shown promising potential for on-orbit image compression. The code is available at https://github.com/Johnathan218/BMNet.