Data-augmented matched subspace detector for hyperspectral subpixel target detection

Abstract

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Novel hyperbolic clustering-based band hierarchy (HCBH) for effective unsupervised band selection of hyperspectral images

“…In Although the effectiveness of our HCBH method has been demonstrated by classification performance of selected bands in the experimental part, the HCBH has not been performed with other unsupervised tasks, such as anomaly detection [34,35,36] and object tracking [37]. For these applications, an effective UBS method can select more discriminative bands whilst reducing the computation cost.…”

A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained Model

“…Li et al [36] proposed a data augmentation method for pixel block pairs (PBP), which greatly increased the number of training samples for hyperspectral images. Yang et al [37] proposed data augmented matched subspace detector (DAMSD) and data augmented MSDinter (DAMSDI) to solve the issue of target spectral scarcity. To address insufficiently labeled samples in practical spectroscopic measurements, Mu et al [38] proposed a conditional variational autoencoder (CVAE).…”

Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

“…The vehicles were occupied at most a few pixels, but fabric panels F1 and F2 were nearly a full pixel while fabric panels F3 and F4 were occupied less than a pixel. The Cooke City dataset is still a challenging dataset for hyperspectral target detection (Yang et al, 2020). All related datasets can be found in https://rslab.ut.ac.ir.…”