OBIA Flashcards
Objects and human interpretation, objects are?
- Objects are primitives that form an image
- Humans see objects, with meaning in real world, rather than pixels
- Humans use shape, texture, colour, context etc. for understanding objects
Image objects are what?
- Basic entities w/in an image that are composed of H-res pixel groups
L-res vs. H-res
- L: 1 pixel composed of many integrated objects
- H: 1 object composed of many individual pixels
H-res pixel groups in image objects
- Each H-res pixel group possesses an intrinsic size, shape, and geographic relationship w/ real-world scene components it models
What is are key drivers to image objects?
- Very high resolution satellite imagery
- Integration with GIS
- Added dimensions to analysis and application
- Solution to MAUP
Pixels vs. objects in GIS
- Pixel = Raster (spectral response, DN)
Object = Vector (Spectral variables, shape variables, texture variables, context)
Added Dimensions to analysis and application using objects
- Classification
- Change detection
- Modelling
- Statistical analysis
- and more
Spectral variables
- Features related to the values of pixels within an object
- Statistical properties (mean, variance, etc.)
Shape variables
- Features related to the shape of an object
- Area, length, width, border, length, shape index, direction, asymmetry, curvature, compactness, etc.
Texture variables
- Features related to spatial patterns of pixels within an image object
- GLCM texture measures etc.
Context variables
- Features related to the spatial setting of an object w/in a scene and/or it’s relationship to other objects in that scene
- Proximity, distance to neighbour, etc.
A 1:1 correspondence between image objects and geographic entity it represents (e.g. lake, field, etc.) means what?
- That the correct spatial scale is being used
- But difficult to attain w/ perfection b/c objects at different scale levels are extracted from same image
Image object
Discrete region of a digital image that is internally coherent and different from its surroundings
What are the 3 traits of image objects for segmentation?
- Discreteness (separable and unique from neighbour)
- Coherence (internal is uniform)
- Contrast (DN is different from neighbour)
Goal of segmentation, and what are geo-objects, what does it require?
- Image objects represent geo-objects
- Geo-objects are identifiable and bounded geographic region (forest, lake, tree, etc.)
- Requires successful segmentation procedure unique to application
What are the ingredients for segmentation?
- Segmentation algorithm
- Expert knowledge of your image and intended application
What are the directions for segmentation?
- Calibrate, ie trial and error, until segments (image objects) represent geo-objects
What are the 2 types of segmentation algorithms used in image processing?
- Discontinuity based, Similarity based
Discontinuity based segmentation algorithms
- Image is partitioned based on abrupt changes in intensity
- Edge detection and enhancement, Limited use in OBIA
Similarity based segmentation algorithms
- Image is partitioned into regions based on a set of pre-defined criteria
- Uses spectral and/or spatial information
- Methods include thresholding, region-based, watershed
Segmentation: Laplacian Edge Detection (Discontinuity based)
- 2D, 2nd order derivative between adjaent pixels
- Directionally invariant edge detection
- Convolution mask is used
- Basically replaces center pixel value as a function of weighted pixels surrounding
- Kernals can be 3x3, 7x7, etc.
- Additional weight to center pixel, 0 weight to diagonal pixels
Segmentation: Sobel Edge Detection (Discontinuity based)
- Stronger gradients are brighted, finds discontinuities and highlights edges
Segmentation: Thresholding (Similarity Based)
- Partitioning of image histograms
- Uses spectral info only, - Good for separating distinct objects from image background
- Can get binary image with 0 below threshold and 1 above
- Multi-thresholding is more complex
What can be used to get melt-pond fraction coverage on sea ice? Why are melt ponds important?
- Single thresholding segmentation using binary image of 0 below threshold, 1 above
- Meltponds are windows to ocean below and transmit light to increase primary productivity