論文解讀：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 選用波段選擇的原因：保留了包含在HSI中的主要物理屬性，魯棒性強。 波段選擇分爲有監督和無監督：無監督又可分爲基於排序和基於聚類。基於排序的無監督波段選擇方法主要是根據某個度量準則來評價每個波段的重要性，如非高斯度，方差，互信息等；基於聚類的無監督波
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相關文章
- 1. 論文閱讀：Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection
- 2. Variable precision rough set based unsupervised band selection technique for hyperspectral image cla
- 3. Hyperspectral Band Selection by Multitask Sparsity Puisuit
- 4. 調程序感悟：Group lasso-based band selection for hyperspectral image classification
- 5. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 6. [論文解讀]An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints
- 7. [論文解讀]Test Selection for Deep Learning Systems
- 8. 論文閱讀《ASSESSMENT OF FEATURE SELECTION METRICS FOR SENTIMENT ANALYSES: TURKISH MOVIE REVIEWS》
- 9. Hyperspectral Image Classification in the Presence of Noisy Labels
- 10. 【論文淺讀】《Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification》