高光谱校准检测：一种结合无监督增量安全伪标签实现的变化检测新方法
Hyperspectral Calibration Detection: A Novel Concept For Change Detection With Unsupervised Incremental Safe Pseudo-Labeling Implementation
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中文总结 AI 辅助
针对车载边缘计算需零标注的高光谱变化检测场景，提出HyperLUCID无监督算法，通过迭代安全收集未变化像素优化光谱校准函数，效率领先且在真实数据集上达最先进准确率
中文摘要 AI 辅助
高光谱变化检测（HCD）已在土地覆盖监测等诸多关键领域得到应用。多数基准HCD算法为半监督方法，部分甚至能在极低样本标注率下运行，但在车载边缘计算需即时检测响应等实际场景中，因车载无法获取新采集图像的真值标注，需满足零标注要求。本研究提出一种完全无监督的HCD算法及轻量模型，适用于车载检测任务。基于迭代扩充的训练集（安全收集部分未变化像素样本），学习迭代优化的光谱校准函数，最终补偿双时相图像中常见的采集条件变异，从而通过分析校准后的光谱轻松检测变化像素。所提高光谱循环无监督校准与增量检测（HyperLUCID）算法不仅计算效率高（比多数基准HCD方法快约1至2个数量级），还在多个真实基准HCD数据集上达到了最先进结果（总体准确率约93.6%至97.9%）。源代码：this https URL。
英文摘要
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.
