从高光谱PRISMA数据中检测粘液的基于多尺度超像素的最近子空间分类器”的Matlab代码.rar

# MSBNSC This repository contains MATLAB code for implementing the "Multi-scale superpixel-based nearest subspace classifier for mucilage detection from hyperspectral PRISMA data" If you use the code, please cite the paper given below, https://authors.elsevier.com/a/1jN9Q3l0%7Eht4K7 @article{TOKER2024104657, title = {Multi-scale superpixel-based nearest subspace classifier for mucilage detection from hyperspectral PRISMA data}, journal = {Digital Signal Processing}, volume = {154}, pages = {104657}, year = {2024}, issn = {1051-2004}, doi = {https://doi.org/10.1016/j.dsp.2024.104657}, url = {https://www.sciencedirect.com/science/article/pii/S1051200424002823}, author = {Kemal Gürkan Toker and Seniha Esen Yuksel}, keywords = {Hyperspectral image classification (HSIC), Nearest subspace classifier (NSC), Canonical correlation analysis (CCA), Multiscale superpixel segmentation, Mucilage detection, Sea of Marmara} } # Usage To utilize this code, follow these steps: 1. Download the (Entropy Rate Superpixel Segmentation) ERS Code: * Access the ERS code from the following URL: https://github.com/mingyuliutw/EntropyRateSuperpixel * Download the code from the provided link. 2. Organize ERS Code: * Place the downloaded ERS code files into a subfolder named "Entropy Rate Superpixel Segmentation" within your project directory. 3. Download the mucilage dataset provided below link and place it into the folder with the same directory of MSBNSC.m. * You can download the mucilage dataset as a mat file to use in Matlab from Google Drive by clicking [here](https://drive.google.com/file/d/1Is062SwtxdFiwFcy_47Qx6lxthIPuicw/view?usp=drive_link). 5. Run the Classifier: * Use the main_MSBNSC_ALL.m to run the Multi-Scale Superpixel Based Nearest Subspace Classifier # Notes * Please ensure that the ERS code is correctly placed in the designated subfolder to enable seamless integration with the classifier. * Refer to the documentation within the ERS repository for additional information on its usage and functionalities. If you use the dataset, please cite the papers given below, @article{TOKER2024104657, title = {Multi-scale superpixel-based nearest subspace classifier for mucilage detection from hyperspectral PRISMA data}, journal = {Digital Signal Processing}, volume = {154}, pages = {104657}, year = {2024}, issn = {1051-2004}, doi = {https://doi.org/10.1016/j.dsp.2024.104657}, url = {https://www.sciencedirect.com/science/article/pii/S1051200424002823}, author = {Kemal Gürkan Toker and Seniha Esen Yuksel}, keywords = {Hyperspectral image classification (HSIC), Nearest subspace classifier (NSC), Canonical correlation analysis (CCA), Multiscale superpixel segmentation, Mucilage detection, Sea of Marmara} } @inproceedings{10.1117/12.2622287, author = {Bahri Abaci and Murat Dede and Seniha Esen Yuksel and Mete Yilmaz}, title = {{Mucilage detection from hyperspectral and multispectral satellite data}}, booktitle = {Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII}, editor = {Miguel Velez-Reyes and David W. Messinger}, organization = {International Society for Optics and Photonics}, publisher = {SPIE}, pages = {120940H}, keywords = {Multispectral, Hyperspectral, Sentinel-2, PRISMA, Mucilage, Sea snot, Sea saliva, Deep learning}, year = {2022}, doi = {10.1117/12.2622287}, URL = {https://doi.org/10.1117/12.2622287} }