{"id":818,"date":"2014-05-02T10:00:30","date_gmt":"2014-05-02T15:00:30","guid":{"rendered":"http:\/\/gisgeography.com\/?p=818"},"modified":"2025-04-01T16:50:12","modified_gmt":"2025-04-01T21:50:12","slug":"image-classification-techniques-remote-sensing","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/image-classification-techniques-remote-sensing\/","title":{"rendered":"Image Classification Techniques in Remote Sensing [Infographic]"},"content":{"rendered":"\n
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“Image classification is the process of assigning land cover classes to pixels. For example, classes include water, urban, forest, agriculture, and grassland.”<\/p>\n<\/blockquote>\n\n\n\n

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What is Image Classification in Remote Sensing?<\/h2>\n\n\n\n

The 4 main types of image classification<\/strong> techniques in remote sensing are:<\/p>\n\n\n\n

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  1. Unsupervised image classification<\/li>\n\n\n\n
  2. Supervised image classification<\/li>\n\n\n\n
  3. Object-based image analysis<\/li>\n\n\n\n
  4. Deep learning object detection<\/li>\n<\/ol>\n\n\n\n

    Unsupervised and supervised image classification are the two most common approaches. <\/p>\n\n\n\n

    However, object-based classification and deep learning has gained more popularity because it’s useful for high-resolution data.<\/p>\n\n\n\n

    Jump To:<\/strong> Unsupervised classification<\/a> | Supervised classification<\/a> | Object-based image analysis<\/a> | Deep learning object detection<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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    1. Unsupervised Classification<\/h3>\n\n\n\n

    In unsupervised classification, it first groups pixels into “clusters” based on their properties. Then, you classify each cluster with a land cover class.<\/p>\n\n\n\n

    Overall, unsupervised classification is the most basic technique. Because you don’t need samples for unsupervised classification, it’s an easy way to segment and understand an image.<\/p>\n\n\n\n

    The two basic steps for unsupervised classification are:<\/p>\n\n\n\n