Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting

TL;DR

This paper introduces Hyper-EI, a self-supervised hyperspectral image inpainting method that effectively restores incomplete hyperspectral images without extensive training data, achieving state-of-the-art results.

Contribution

The paper presents Hyper-EI, a novel self-supervised inpainting algorithm for hyperspectral images that does not require large datasets or pre-trained models.

Findings

Achieves state-of-the-art inpainting performance.

Does not require extensive training datasets.

Effective for real-world incomplete hyperspectral data.

Abstract

Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology for HSI in remote sensing applications is based on the push-broom scanning approach in which the camera records the spectral image of a stripe of the scene at a time, while the image is generated by the aggregation of measurements through time. In real-world airborne and spaceborne HSI instruments, some empty stripes would appear at certain locations, because platforms do not always maintain a constant programmed attitude, or have access to accurate digital elevation maps (DEM), and the travelling track is not necessarily aligned with the hyperspectral cameras at all times. This makes the enhancement of the acquired HS images from incomplete or corrupted observations an essential task. We introduce a novel HSI inpainting algorithm here, called Hyperspectral Equivariant Imaging (Hyper-EI). Hyper-EI is a self-supervised learning-based method which does not require training on extensive datasets or access to a pre-trained model. Experimental results show that the proposed method achieves state-of-the-art inpainting performance compared to the existing methods.