The large size of hyperspectral imaging poses a significant threat to its potential use in real
life due to the abundant information stored in it. The use of deep learning for such data
processing is visible in recent applications. In this work, we propose a lossy hyperspectral
image compression algorithm based on the concept of autoencoders. It uses a combination
of the convolution layer and max-pooling layer to reduce the dimensions of the input image
and generate a compressed image. The original image with some loss of information is …