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Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases.
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data.
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment.
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands.
Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images.
