Ning WANG, Wenxing BAO, Kewen QU, Wei FENG. Hyperspectral unmixing with shared endmember variability in homogeneous region[J]. Optics and Precision Engineering, 2024, 32(4): 578

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- Optics and Precision Engineering
- Vol. 32, Issue 4, 578 (2024)

Fig. 1. Effect of different elements on endmembers

Fig. 2. Diagram of SEVU algorithm

Fig. 3. Datasets

Fig. 4. Effect of signal-to-noise ratio on algorithm performance

Fig. 5. Effect of parameters on SEVU algorithm performance

Fig. 6. Number of blocks in the image after superpixel segmentation

Fig. 7. Endmember bundles constructed by SEVU algorithm (Synthetic dataset SNR_30 dB)

Fig. 8. Abundance estimated by each algorithm (Synthetic dataset SNR_30 dB)

Fig. 9. Endmember bundles constructed by SEVU algorithm (Jasper Ridge)

Fig. 10. Abundance estimated by each algorithm (Jasper Ridge)

Fig. 11. Partial endmember bundles constructed by SEVU algorithm (Cuprite)

Fig. 12. Partial abundance estimated by each algorithm (Cuprite)

Fig. 13. Profiles of the objective function with the number of iterations of the algorithm

Fig. 14. Comparison of algorithm run times (s)
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Table 1. Effect of number of superpixel blocks on mSAD, aRMSE, yRMSE
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Table 1. [in Chinese]
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Table 2. Evaluation results of each algorithm for mSAD (Synthetic dataset SNR_30 dB)
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Table 3. Evaluation results of each algorithm for aRMSE and yRMSE (Synthetic dataset SNR_30 dB)
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Table 4. Evaluation results of each algorithm for mSAD (Jasper Ridge)
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Table 5. Evaluation results of each algorithm for aRMSE and yRMSE (Jasper Ridge)
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Table 6. Evaluation results of each algorithm for yRMSE (Cuprite)
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Table 7. Evaluation results of each algorithm for mSAD (Cuprite)

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