Surveying and Geomatics | 5. Texture by Abraham | Learn Smarter
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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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Sections

  • 2

    Texture

    Texture refers to the arrangement and variation of tones in an image, influencing the overall appearance and discernibility of visual features.

    Learning Practice

  • 3

    Pattern

    The section on pattern focuses on the spatial arrangement and repetition of objects, highlighting its significance in distinguishing features in images.

    Learning Practice

  • 4

    Shape

    Shape refers to the distinct form or outline of objects, serving as a critical clue in image interpretation.

    Learning Practice

  • 5

    Size

    This section discusses the concept of size in remote sensing, particularly how it relates to the scale of images, and its significance in distinguishing various features.

    Learning Practice

  • 6

    Shadow

    The section discusses the significance of shadow in the interpretation of images, detailing how it can assist in determining object height and identifying shapes while also acknowledging the limitations shadows impose.

    Learning Practice

  • 7

    Site/association

    This section explores the concepts of site and association in remote sensing, detailing their significance in spatial analysis.

    Learning Practice

  • 5.17

    Digital Image Interpretation Methods

    This section focuses on digital image interpretation methods for processing optical remote sensing images, highlighting techniques for effective image analysis.

    Learning Practice

  • 5.17.1

    Image Pre-Processing

    Image pre-processing is the initial stage of processing raw image data to correct for geometric distortions, calibrate radiometric data, and remove noise.

    Learning Practice

  • 5.17.1.A

    Geometric Corrections

    This section discusses the fundamental steps involved in geometric corrections of digital images used in remote sensing.

    Learning Practice

  • 5.17.1.A.i

    Georeferencing

    Georeferencing is the process of aligning digital images with their corresponding geographic coordinates to enable accurate analysis and comparison.

    Learning Practice

  • 5.17.1.A.ii

    Resampling

    Resampling is the process of altering the pixel values of an image to match a new coordinate system following georeferencing, essential for maintaining accuracy in digital image processing.

    Learning Practice

  • 5.17.1.B

    Atmospheric Correction

    Atmospheric correction is a crucial step in modifying digital numbers (DN) in remote sensing images to mitigate the noise effects introduced by the atmosphere.

    Learning Practice

  • 5.17.2

    Image Enhancement

    Image enhancement aims to improve the quality and interpretability of images by increasing contrast without adding information.

    Learning Practice

  • 5.17.2.A

    Image Histogram

    An image histogram is a graphical representation that shows the distribution of digital numbers (DN) in an image, which aids in image enhancement and interpretation.

    Learning Practice

  • 5.17.2.B

    Contrast Enhancement

    This section discusses the importance of contrast enhancement in image processing, focusing on techniques and methods for improving visual quality and interpretability of images.

    Learning Practice

  • 5.17.2.C

    Image Transformations

    Image transformations use mathematical functions to create new images that enhance specific features of original images.

    Learning Practice

  • 5.17.3

    Digital Image Classification

    This section discusses digital image classification methods for optical and microwave images, focusing on their spectral signatures and classification techniques.

    Learning Practice

  • 5.17.3.A

    Supervised Classification

    Supervised classification involves identifying known classes in digital images to categorize all pixels based on their spectral signatures.

    Learning Practice

  • 5.17.3.B

    Unsupervised Classification

    Unsupervised classification is a method of digital image classification based purely on the spectral properties of pixels.

    Learning Practice

  • 5.17.4

    Accuracy Assessment

    This section emphasizes the importance of accuracy assessment in remote sensing image classification, addressing potential errors and methods for evaluating classification quality.

    Learning Practice

References

5d.pdf

Class Notes

Memorization

What we have learnt

  • Texture is a critical aspec...
  • Digital image processing in...
  • Both supervised and unsuper...

Final Test

Revision Tests