Abstract
The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learning method which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates.
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Wang, K., Zhang, N., Hu, K., Cao, T. (2022). Multispectral Image Compression Algorithm Based on Sliced Convolutional LSTM. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-19-0386-1_54
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DOI: https://doi.org/10.1007/978-981-19-0386-1_54
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