5. Texture
The chapter introduces key concepts in image interpretation, outlining the significance of texture, pattern, shape, size, shadow, and site/association. It further explores digital image interpretation methods, emphasizing the differences between visual and digital techniques, and details the processes of image pre-processing, enhancement, transformations, and classification. An assessment of accuracy is critical for evaluating the quality of classified maps derived from remote sensing data.
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What we have learnt
- Texture is a critical aspect for visualizing smoothness or coarseness in images.
- Digital image processing involves several stages, including pre-processing, enhancement, transformation, and classification.
- Both supervised and unsupervised classifications have their applications and relevance in remote sensing image analysis.
Key Concepts
- -- Texture
- The arrangement and frequency of tonal variation in an image that helps determine the overall smoothness or coarseness of features.
- -- Georeferencing
- The process of converting image coordinates to ground coordinates to remove distortions caused by sensor geometry.
- -- Supervised Classification
- A classification method where an analyst uses a priori knowledge to identify training sites and classify pixels based on their DN values.
- -- Unsupervised Classification
- A classification method that groups DN values without the need for prior knowledge of specific land cover types.
- -- Error Matrix
- A tool used for assessing the accuracy of a classification by comparing classified data against reference data.
Additional Learning Materials
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