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Classification groups image pixels into classes based on spectral and spatial traits. Valuable data, like land cover and features, are extracted from satellite images for mapping.
Classification refers to the process of categorising or grouping pixels within an image into distinct classes or categories based on their spectral characteristics and spatial patterns. This technique is commonly used to extract valuable information from satellite images, such as land cover, land use, natural features, and human-made structures.
Satellite imagery classification involves assigning each pixel in an image to a specific class, such as water bodies, forests, urban areas, agricultural fields, and more. This helps in creating thematic maps that provide insights into various aspects of the Earth’s surface and its changes over time.
We use Machine Learning (ML) to classify features within imagery. We are adept as using both pixel-based and object-based ML approaches understanding the benefits and limitations of those methods. With years of experience, our team can ensure the most accurate classification results are achieved and we are constantly refining and improving our methods. We also know that ML will not provide 100% accuracy which is why we include human quality control as part of our overall process.
ER Solutions solutions have the capability to analyze and interpret satellite images for a diverse array of uses, which encompass:
Image ProcessingMost types of raw satellite imagery require some type of geometric correction or rectification so that the image corresponds to real-world
Spectral AnalysisSatellites use diverse wavelengths to reveal unseen wonders. Multispectral/hyperspectral imagery provides valuable insights for vegetation, minerals, and more. Revealing the unseen: