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 reconstructed using transpose convolution layer that uses reverse of the procedure used by the encoder. The compressed image has been entropy coded using an adaptive arithmetic coder for transmission or storage application. The method provides an improvement of 28% in PSNR with 21 times increment in the compression ratio. The effect of compression on classification has also been evaluated in the experiment using state of art classification algorithm. Negligible difference in classification accuracy was obtained that proves the effectiveness of the proposed algorithm.

由于存储在其中的大量信息，高光谱成像的大尺寸对其在现实生活中的潜在使用构成了重大威胁。在最近的应用程序中可以看到将深度学习用于此类数据处理。在这项工作中，我们提出了一种基于自动编码器概念的有损高光谱图像压缩算法。它使用卷积层和最大合并层的组合来减小输入图像的尺寸并生成压缩图像。使用转置卷积层重建信息丢失的原始图像，转置卷积层使用与编码器相反的过程。已使用自适应算术编码器对压缩图像进行了熵编码，以用于传输或存储应用程序。该方法将PSNR提高了28％，压缩率提高了21倍。压缩对分类的影响也已在实验中使用最新的分类算法进行了评估。获得的分类精度差异可忽略不计，证明了所提算法的有效性。