{"id":21340981,"url":"https://github.com/aashishrai3799/remote-sensing-image-classification","last_synced_at":"2025-07-12T14:33:39.385Z","repository":{"id":160201353,"uuid":"193575644","full_name":"aashishrai3799/Remote-sensing-image-classification","owner":"aashishrai3799","description":"classification of remote sensing images using Convolutional Neural Networks (CNN)","archived":false,"fork":false,"pushed_at":"2020-04-14T15:49:34.000Z","size":49,"stargazers_count":13,"open_issues_count":0,"forks_count":8,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-01-29T21:14:19.056Z","etag":null,"topics":["classify-images","cnn","convolutional-neural-networks","image-classification","remote-sensing","rsi-cb","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aashishrai3799.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"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":"2019-06-24T20:31:36.000Z","updated_at":"2023-11-15T02:44:56.000Z","dependencies_parsed_at":"2023-05-23T15:15:33.394Z","dependency_job_id":null,"html_url":"https://github.com/aashishrai3799/Remote-sensing-image-classification","commit_stats":{"total_commits":13,"total_committers":2,"mean_commits":6.5,"dds":"0.46153846153846156","last_synced_commit":"571c05f00758c36707d0bd8f7f79230cc1e4e863"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aashishrai3799%2FRemote-sensing-image-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aashishrai3799%2FRemote-sensing-image-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aashishrai3799%2FRemote-sensing-image-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aashishrai3799%2FRemote-sensing-image-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aashishrai3799","download_url":"https://codeload.github.com/aashishrai3799/Remote-sensing-image-classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225824809,"owners_count":17529906,"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":["classify-images","cnn","convolutional-neural-networks","image-classification","remote-sensing","rsi-cb","tensorflow"],"created_at":"2024-11-22T00:54:17.411Z","updated_at":"2024-11-22T00:54:19.275Z","avatar_url":"https://github.com/aashishrai3799.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Remote-sensing-image-classification\nclassification of remote sensing images using Convolutional Neural Networks (CNN)\n\n#### Dataset used: RSI-CB (A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data)\n\n## Introduction\nRemote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object\nand thus in contrast to on-site observation, especially the Earth.\nIn this model supervised method of image classification is used for classifying remote sensing images. Experiments were carried out on the\ndataset provided and has been tested against different test images.\n\n## Working\nThe Tensorflow model is build using a Convolutional Neural Network(CNN), and is trained using 8 different classes of RSI-CB\ndataset. Hence, it is able to classify images into 8 different classes. The input to the model is a ?x64x64x3 RGB-image-vector,\nand output is a ?x8 vector. Each row of the output vector corresponds to a different class.\n\nTrain accuracy: 99.3%\n\nTest accuracy: 99%\n\n#### Platform: Python 3.7\n\n## Libraries\n*   numpy      - 1.16\n*   matplotlib - 3.0.3\n*   tensorflow - 1.14\n*   PIL        - 4.3.0\n*   tqdm\n*   pyunpack\n*   urllib\n*   time\n*   os\n*   math\n\n\n#### I trained the model on Tesla T4 GPU, it took around 11 seconds for 5 epochs.\n\n##### NOTE: Contact if you get HTTP error while downloading RSI-CB dataset\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faashishrai3799%2Fremote-sensing-image-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faashishrai3799%2Fremote-sensing-image-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faashishrai3799%2Fremote-sensing-image-classification/lists"}