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BTC-Net: Efficient Bit-Level Tensor Data Compression Network for Hyperspectral Image
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DOI：10.1109/TGRS.2023.3339843.png)
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Abstract
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Now it is still a challenge to compress high-throughput hyperspectral tensor image data on lightweight air-carried/spaceborne remote sensing systems, primarily due to insufficient computational resources and limited transmission bandwidth. To address this challenge, we propose a bit-level tensor data compression network (BTC-Net) that provides higher compression performance by leveraging a data-driven lightweight quantized neural encoder with two-stage bit compression. The BTC-Net achieves semantic near-lossless high reconstruction quality at low compression bit rates thanks to its optimized decoder, which uses a channelwise attention-based enhancement module to recover hyperspectral tensor data. Experimental results on different hyperspectral datasets show that the BTC-Net could achieve an extremely low compression bit rate of fewer than 0.04 bits per pixel per band (bpppb) with the state-of-the-art (SOTA) reconstruction performances.
Keywords:
Bit-level compression
deep neural network (DNN)
feature enhancement
tensor data
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