Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 162, Issue -, Pages 699-708Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2019.05.014
Keywords
Plant hyperspectral images; Region of interest; Region-based block compressive sensing
Funding
- State Scholarship Foundation of China Scholarship Council
- National Natural Science Foundation of China [U1609218]
- National Key Foundation for Exploring Scientific Instrument of China [61427808]
- National Nature Science Foundation of China [41671415]
- Zhejiang public welfare Technology Application Research Project of China [2016C32087]
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In order to improve the reconstruction effect of plant hyperspectral images, a region-based block compressive sensing (RBCS) algorithm is proposed. Local means and local standard deviations (LMLSD) criterion is used to select the optimal band in the hyperspectral images. The k-means clustering algorithm is introduced to extract the tea regions from the optimal band. And spatial adaptive blocking strategy is involved to realize the optimized spatial blocking only for tea regions in the hyperspectral images. Then discrete cosine transform (DCT) sparse basis and random gaussian measurement matrix are combined to compress the data. Finally, stagewise orthogonal matching pursuit (StOMP) algorithm is used to reconstruct plant hyperspectral images. Peak signal to noise ratio (PSNR), spectrum curve and spectral angle mapper (SAM) and the error of spectral indices are used to evaluate the reconstructed performance in the spatial and spectral domains. Experimental results show that the reconstructed performance of RBCS is significantly better than that of single spectral compressive sensing (SSCS) and block compressive sensing (BCS) at different sampling ratios.
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