{"id":19962063,"url":"https://github.com/syamkakarla98/hyperspectral_image_analysis_simplified","last_synced_at":"2025-04-10T03:55:23.001Z","repository":{"id":37614898,"uuid":"227761839","full_name":"syamkakarla98/Hyperspectral_Image_Analysis_Simplified","owner":"syamkakarla98","description":"The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.","archived":false,"fork":false,"pushed_at":"2023-07-06T21:27:28.000Z","size":22466,"stargazers_count":232,"open_issues_count":4,"forks_count":50,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-10T03:55:12.387Z","etag":null,"topics":["classification","data-analysis","data-science","dimensionality-reduction","hacktoberfest","hyperspectral","hyperspectral-image-classification","hyperspectral-images","indian-pines-dataset","machine-learning","matplotlib-pyplot","pandas","plotly","python","python3","remote-sensing","satellite-imagery","satellite-images","tensorflow","turorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/syamkakarla98.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"Contributing.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2019-12-13T05:17:07.000Z","updated_at":"2025-04-05T03:40:53.000Z","dependencies_parsed_at":"2023-09-24T16:57:11.014Z","dependency_job_id":null,"html_url":"https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified","commit_stats":{"total_commits":55,"total_committers":2,"mean_commits":27.5,"dds":0.1454545454545455,"last_synced_commit":"c6466ef91af89656a447aeae5f5ab2c33063c9b0"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/syamkakarla98%2FHyperspectral_Image_Analysis_Simplified","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/syamkakarla98%2FHyperspectral_Image_Analysis_Simplified/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/syamkakarla98%2FHyperspectral_Image_Analysis_Simplified/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/syamkakarla98%2FHyperspectral_Image_Analysis_Simplified/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/syamkakarla98","download_url":"https://codeload.github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248155001,"owners_count":21056542,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","data-analysis","data-science","dimensionality-reduction","hacktoberfest","hyperspectral","hyperspectral-image-classification","hyperspectral-images","indian-pines-dataset","machine-learning","matplotlib-pyplot","pandas","plotly","python","python3","remote-sensing","satellite-imagery","satellite-images","tensorflow","turorial"],"created_at":"2024-11-13T02:09:20.937Z","updated_at":"2025-04-10T03:55:22.980Z","avatar_url":"https://github.com/syamkakarla98.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hyper Spectral Image(HSI) Analysis Simplified\n  ![Python](https://img.shields.io/badge/Python-3.6-green.svg)\n  ![Stars](https://img.shields.io/github/stars/syamkakarla98/Hyper-Spectral-Image-Analysis-Simplified)\n  ![Forks]( https://img.shields.io/github/forks/syamkakarla98/Hyper-Spectral-Image-Analysis-Simplified)\n  ![issued](https://img.shields.io/github/issues/syamkakarla98/Hyper-Spectral-Image-Analysis-Simplified)\n  ![License](https://img.shields.io/github/license/syamkakarla98/Hyperspectral_Image_Analysis_Simplified)\n  \u003c!-- [![Downloads](https://img.shields.io/github/downloads/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/total.svg)](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified)\n--\u003e\n  \n#### The repository contains the implementation of different machine learning techniques on Hyperspectral and satellite Imagery analysis. Find more articles from [here](https://syamkakarla.medium.com/).\n\n1.[ **Basics**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Basics.ipynb) - This notebook fatures:\n  * **Introduction**\n  * **Downloading HSI**\n  * **Reading** the hyperspecral image.\n  * **Visualizing the bands** of the hyperspectral image.\n  * **Visualizing ground truth** of the image.\n  * **Extracting pixels** of the hyperspectral image.\n  * **Visualizing spectral signatures** of the hyperspectral image.\n\n\n2.[ **Data Analysis**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Data%20Analysis.ipynb) - This notebook fatures data anlysis of the indian pines hyperspectral image:\n  * **Visualizing pixels** of the hyperspectral image.\n  * **Bar plot** w.r.t _class labels_ of the hyperspectral image.\n  * **Box Plot** w.r.t the _class labels_ and _bands_ of hyperspecral image.\n  * **Distribution Plot** w.r.t the _bands_ of hyperspecral image.\n\n3.[**Exploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy**](https://towardsdatascience.com/exploratory-data-analysis-eda-on-satellite-imagery-using-earthpy-c0e186fe4293)\n\n4.**Dimensionality Reduction**\n  * Check this article entitled [Dimensionality Reduction in Hyperspectral Images using Python](https://towardsdatascience.com/dimensionality-reduction-in-hyperspectral-images-using-python-611b40b6accc) and [*code*](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/Articles/Dimensionality_Reduction_on%C2%A0HSI_using_PCA.ipynb).\n  \n  \n  * [ **PCA + SVM**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/PCA%2BSVM.ipynb) - This notebook implements the following _machine learning_ techniques on the indian pines dataset.\n    * **Dimensionality Rreduction**: The _principal component analysis(PCA)_ is used to reduce the dimensions of the dataset.\n    * **Classifier**: The _support vector machine(SVM)_ classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.\n  \n * [ **Kernel PCA + SVM**](https://github.com/syamkakarla98/Hyperspectral_Image_Analysis_Simplified/blob/master/kernel%20PCA%2BSVM.ipynb)  - This notebook implements the following _machine learning_ techniques on the indian pines dataset.\n \n    * **Dimensionality Rreduction**: The _Kernel principal component analysis(PCA)_  with '**rbf kernel**' is used to reduce the dimensionality of the dataset.\n    * **Classifier**: The _support vector machine(SVM)_ classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.\n\n\n\n## Do give a star if you like the repository.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyamkakarla98%2Fhyperspectral_image_analysis_simplified","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsyamkakarla98%2Fhyperspectral_image_analysis_simplified","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsyamkakarla98%2Fhyperspectral_image_analysis_simplified/lists"}