A Universal Representation Mechanism for Multisource Hyperspectral Remote Sensing Image

Deep learning-based processing of hyperspectral remote sensing images (HSIs) has emerged as a research hotspot. In recent years, the delivery of numerous novel HSI acquisition platforms has resulted in an exponential growth in the number of available HSI datasets from various sources. However, due to variations among hyperspectral sensors, the acquired data frequently consists of various spectral dimensions. This results in a challenge since standard deep learning approaches often require a different model for each HSI source, which impedes the construction of fundamental models for HSIs. To address this issue, we propose a unified representation mechanism for multisource HSIs that can transform spectra from numerous dimensions to a shared representation space, yielding a scalable pretraining model. Compared to existing methods, our strategy has the following advantages: 1) compatibility with HSIs of arbitrary spectral dimensions, ranges, and resolutions; 2) fully leveraging existing multisource HSIs; 3) spontaneously capturing spectral features via self-supervised learning; and 4) pretrained on large-scale multisource HSI datasets and a considerable enhancement in classification accuracy. In three reconstruction test sets, the PSNRs are 27.07, 22.27, and 29.35 dB, and the SSIMs are 0.93, 0.82, and 0.88, respectively. Compared with the randomly initialized model, there are 8.28% and 1.4% improvements on the Indian Pines dataset and Pavia University dataset respectively.