  The back-propagation algorithm has long been the de-facto standard in
optimizing weights and biases in neural networks, particularly in cutting-edge
deep learning models. Its widespread adoption in fields like natural language
processing, computer vision, and remote sensing has revolutionized automation
in various tasks. The popularity of back-propagation stems from its ability to
achieve outstanding performance in tasks such as classification, detection, and
segmentation. Nevertheless, back-propagation is not without its limitations,
encompassing sensitivity to initial conditions, vanishing gradients,
overfitting, and computational complexity. The recent introduction of a
forward-forward algorithm (FFA), which computes local goodness functions to
optimize network parameters, alleviates the dependence on substantial
computational resources and the constant need for architectural scaling. This
study investigates the application of FFA for hyperspectral image
classification. Experimental results and comparative analysis are provided with
the use of the traditional back-propagation algorithm. Preliminary results show
the potential behind FFA and its promises.
