IEEE

Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes

Abstract: Deep learning-based informative band selection methods on hyperspectral images (HSIs) have recently gained intense attention to eliminate spectral correlation and redundancies. However, ...
IEEE

Superpixel-Based Autoencoder-Like Nonnegative Tensor Factorization for Hyperspectral Unmixing

Abstract: Hyperspectral unmixing is significant for advancing remote sensing (RS) applications, aiming at extracting the spectra of pure materials (called endmembers) and obtaining their proportions ...
GitHub

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...
GitHub

Anomaly Detection in Stock Prices using LSTM Autoencoder

This project shows how to find anomalies in financial time series data, specifically the stock values of Apple (AAPL), using a LSTM Autoencoder. Stock price anomalies may be a sign of major market ...