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BTC-Net V2: Spatial-Priority Hierarchical Reconstruction for Hyperspectral Image Compression
This repository contains the officially released dataset for BTC-Net V2.
For the complete implementation, network architectures, and pre-trained models, please visit our official GitHub repository: π GitHub: MWan-deeplearner/BTC-Net-V2
π Dataset Overview
This dataset consists of processed Hyperspectral Image (HSI) data (specifically from the AVIRIS sensor), structured and chunked to facilitate deep learning-based image compression and hierarchical wireless transmission research.
- Data Format: MATLAB
.matfiles packed into sequential.zipchunks. - Total Samples: 1,195 processed HSI patches/sub-volumes.
- Core Application: Spatial-priority hierarchical reconstruction, HSI compression, deep learning.
π Repository Structure & Naming Convention
To prevent download interruptions caused by network fluctuations, the 1,195 .mat files are distributed evenly into 12 independent zip archives (approx. 100 files per chunk).
The repository is structured as follows:
.
βββ README.md # This Dataset Card
βββ aviris_mat_chunk_01.zip # Sub-dataset chunk 01 (Contains files 1-100)
βββ aviris_mat_chunk_02.zip # Sub-dataset chunk 02 (Contains files 101-200)
...
βββ aviris_mat_chunk_12.zip # Sub-dataset chunk 12 (Contains remaining files)
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