Effects of a Multi-Sensor Convolutional Autoencoder Model for Lossy Hyperspectral Data Compression on Surveillance Target Detection

This paper analyses the challenge of using the same autoencoder model for lossy compression on different hyperspectral sensors and its impact on the complex application of camouflaged target detection. We propose an adaptive 1D convolutional autoencoder architecture for lossy hyperspectral data compression with the property of portability to unknown spectral signatures of different sensors. In our experiment, a model is pre-trained on PRISMA satellite data from around the world and fine-tuned with a small amount of target HySpex sensor data. The comparison model with the same architecture is trained from scratch with a larger amount of target sensor data. Compression rates of 4, 8, and 16 are implemented and evaluated. The evaluation discusses the reconstruction accuracy measured by the SAM, PSNR, and SSIM metrics. In addition, the reconstruction error is evaluated using the camouflaged target detection application. We show that fine-tuning the pretrained model with a small amount of data results in higher reconstruction accuracy than building the model from scratch with a larger amount of target data. This observation also applies to the target detection application.