Hyperspectral Calibration Detection: A Novel Concept For Change Detection With Unsupervised Incremental Safe Pseudo-Labeling Implementation.

Hyperspectral change detection (HCD) has found numerous key applications, such as land cover monitoring. The majority of benchmark HCD algorithms are semi-supervised methods, and some of them can even achieve very low sample labeling rates. However, in some practical scenarios, such as those requiring immediate detection responses for onboard edge computing, we need to achieve the zero-label requirement as ground-truth labeling would not be available onboard for newly acquired images. In this work, we propose a fully unsupervised HCD algorithm, together with a lightweight model, quite suitable for onboard detection missions. Based on an iteratively augmented training set that safely collects some unchanged pixel samples, we learn an iteratively refined spectrum calibration function that eventually compensates the variability of acquisition conditions (often observed in bitemporal images), thereby making the changed pixels easily detectable by analyzing the calibrated spectra. The proposed hyperspectral looping unsupervised calibration and incremental detection (HyperLUCID) algorithm is not only computationally efficient (around 1 to 2 orders of magnitude faster than most benchmark HCD methods), but has also achieved state-of-the-art results (around 93.6% to 97.9% overall accuracy) on several real benchmark HCD datasets. Source codes: https://github.com/IHCLab/HyperLUCID.