HyLiOSR: Staged Progressive Learning for Joint Open-Set Recognition of Hyperspectral and LiDAR Data
Bobo Xi, Mingshuo Cai, Jiaojiao Li, Zhengjue Wang, Shou Feng, Yunsong Li, Jocelyn Chanussot
Abstract
The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data have seen significant advancements in recent research. However, it would be more practical if we could simultaneously detect the unknown classes in a more realistic open-set scenario. In this article, we introduce a novel open-set recognition (OSR) method for HSI and LiDAR data, termed HyLiOSR, which devises a staged progressive learning strategy to effectively bridge the gap between closed-set and open-set feature distributions within an autoencoder framework. Specifically, for the first stage, the reconstruction-based network is dedicated to accurately modeling each known category by learning multiple Gaussian prototypes, which facilitates OSR by disentangling the distribution of known classes. In the second stage, we actively synthesize samples of unknown classes during the feature extraction phase and create a virtual unknown classifier, enabling the network to effectively differentiate between known and unknown class samples. This approach establishes a distinct separation between known and unknown classes in the latent feature space, thereby enhancing the capability of the frameworks to distinguish between them. Comprehensive experiments conducted on three benchmark datasets demonstrate that the proposed HyLiOSR outperforms existing state-of-the-art methods. The source code will be accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/B-Xi/TGRS_2025_HyLiOSR</uri>.