A Shared Encoder for Multi-Source Hyperspectral Images

Weili Kong, Baisen Liu, Xiaojun Bi, Jiaming Pei

Keywords: Multi-Source Hyperspectral Images; Shared Feature Space; Unified Representation.

TL;DR: We introduce a shared encoder that maps all multi-source hyperspectral images into a unified feature space, establishing a universal framework for the representation of multi-source HSIs.

Abstract:

Multi-source hyperspectral images(HSIs) which captured from diverse sensors commonly possess varying bands.
When employing deep learning techniques for their processing, individual models are necessitated for each source due to the disparate dimensions.
To tackle this problem, we propose a shared encoder to project all HSIs into a unified feature space.
It establishes a general framework for the representation of multi-source HSIs, providing foundational conditions for the development of a universal HSI analysis model.

Submission Number: 62