论文解读：BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image 选用波段选择的原因：保留了包含在HSI中的主要物理属性，鲁棒性强。 波段选择分为有监督和无监督：无监督又可分为基于排序和基于聚类。基于排序的无监督波段选择方法主要是根据某个度量准则来评价每个波段的重要性，如非高斯度，方差，互信息等；基于聚类的无监督波
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相关文章
- 1. 论文阅读：Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection
- 2. Variable precision rough set based unsupervised band selection technique for hyperspectral image cla
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- 4. 调程序感悟：Group lasso-based band selection for hyperspectral image classification
- 5. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 6. [论文解读]An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints
- 7. [论文解读]Test Selection for Deep Learning Systems
- 8. 论文阅读《ASSESSMENT OF FEATURE SELECTION METRICS FOR SENTIMENT ANALYSES: TURKISH MOVIE REVIEWS》
- 9. Hyperspectral Image Classification in the Presence of Noisy Labels
- 10. 【论文浅读】《Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification》