SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing

no code implementations • 17 Nov 2023 • Soham Chitnis, Kiran Mantripragada, Faisal Z. Qureshi

The Hyperspectral Unxming problem is to find the pure spectral signal of the underlying materials (endmembers) and their proportions (abundances).

Hyperspectral Unmixing Transfer Learning

Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images

no code implementations • 5 Mar 2022 • Kiran Mantripragada, Faisal Z. Qureshi

The results demonstrate the potential of our method to find objects in a Hyperspectral Image while bypassing the burden of manual search of the optimal parameters.

Image Segmentation Segmentation +1

Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder

no code implementations • 2 Mar 2022 • Kiran Mantripragada, Faisal Z. Qureshi

We present a method for hyperspectral pixel {\it unmixing}.

Transfer Learning

The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study

no code implementations • 1 Apr 2021 • Kiran Mantripragada, Phuong D. Dao, Yuhong He, Faisal Z. Qureshi

We use five dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional hyperspectral pixels.

Classification Dimensionality Reduction +2