用隐式神经表示压缩多光谱卫星图像，提升细节保留与效率
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
- 将卫星图像建模为坐标空间的连续函数，适应不同分辨率
- 引入傅里叶调制机制，动态适配各波段的光谱与空间特征
- 专为多光谱数据设计，适合遥感图像压缩与分析场景
多光谱卫星图像在农业、渔业和环境监测中至关重要。然而，其高维性、海量数据量以及多通道间差异化的空间分辨率给数据压缩与分析带来显著挑战。本文提出ImpliSat框架，通过高效压缩与重建多光谱卫星数据来应对这些挑战。ImpliSat利用隐式神经表示（INR）将卫星图像建模为坐标空间上的连续函数，捕捉跨不同空间分辨率的精细空间细节。此外，我们提出一种傅里叶调制算法，可动态适应各波段的光谱与空间特性，确保在压缩过程中最大限度保留关键图像细节。
原文摘要 · Abstract (English)
Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels pose significant challenges for data compression and analysis. This paper presents ImpliSat, a unified framework specifically designed to address these challenges through efficient compression and reconstruction of multispectral satellite data. ImpliSat leverages Implicit Neural Representations (INR) to model satellite images as continuous functions over coordinate space, capturing fine spatial details across varying spatial resolutions. Furthermore, we introduce a Fourier modulation algorithm that dynamically adjusts to the spectral and spatial characteristics of each band, ensuring optimal compression while preserving critical image details.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。