Triple Contrastive Representation Learning for Hyperspectral Image Classification With Noisy Labels

Zhang, Xinyu; Yang, Shuyuan; Feng, Zhixi; Song, Liangliang; Wei, Yantao; Jiao, Licheng

doi:10.1109/tgrs.2023.3292142

Cited by 5 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As opposed to visible and multispectral images, hyperspectral images (HSIs) have been regarded as a remarkable invention in the field of remote sensing imaging sciences, due to their practical capacity to capture high-dimensional spectral information from different scenes on the Earth's surface [1]. HSIs consist of innumerable contiguous spectral bands that span the electromagnetic spectrum, providing rich and detailed physical attributes of land covers, which facilitate the development of various applications such as change detection [2][3][4][5], land-cover classification [6][7][8], retrieval [9], scene classification [10,11] and anomaly detection [12,13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

Wang,

Shi,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

show abstract