調程序感悟：Group lasso-based band selection for hyperspectral image classification
要想跑通這篇文章的程序，關鍵的三個點如下： 1、一定記住！一定記住！一定記住！一定要提前裝好Visual Studio（PS：之前嫌VS2017太佔空間，所以卸載了，結果導致程序搞了一早上），因爲跑這個代碼的glmnet、libsvm兩個工具包需要VS編譯環境的支持。 2、按照作者給出的鏈接我們可以下載好glmnet、libsvm的壓縮包，然後將它們解壓到MATLAB的工具包路徑裏面，這裏需要你找
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相關文章
- 1. Variable precision rough set based unsupervised band selection technique for hyperspectral image cla
- 2. Hyperspectral Band Selection by Multitask Sparsity Puisuit
- 3. Generative Adversarial Networks for Hyperspectral Image Classification
- 4. IGARSS2017_SSRN 讀後感DEEP RESIDUAL NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
- 5. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 6. 論文解讀：BS-Nets: An End-to-End Framework For Band Selection of Hyperspectral Image
- 7. Paper Reading -- 《Spectral-Spatial Attention Networks for Hyperspectral Image Classification》
- 8. 論文閱讀：Scalable One-Pass Self-Representation Learning for Hyperspectral Band Selection
- 9. A new deep convolutional neural network for fast hyperspectral image classification Review
- 10. Deep Learning for Hyperspectral Image Classification:An Overview