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Various Change Detection Analysis Techniques. Broadly Divided in Two Approaches …. Post Classification Approach. Pre Classification Approach. Post Classification Approach. Involves the analysis of differences between two independent categorization products. Two Methods: Advantages:

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broadly divided in two approaches

Broadly Divided in Two Approaches …..

Post Classification Approach.

Pre Classification Approach.

post classification approach
Post Classification Approach
  • Involves the analysis of differences between two independent categorization products.

Two Methods:

Advantages:

  • Data Normalization not required.

Disadvantages:

  • Consistency.
  • Error propagation.
  • Spectral or Spectral-spatial (unsupervised) classification.
  • Ground truth (supervised) classification.
pre classification approach
Pre Classification Approach
  • High Classification accuracy – for areas having significantly different spectral distributions in the image data.

Various Pre Classification Analysis Approaches:

  • Composite Analysis.
  • Image Differencing.
  • Principal Components Analysis.
  • Change Vector Analysis.
  • Spectral Mixture Analysis.
  • Inner Product Analysis.
  • Correlation Analysis (Spectral Signature Similarity).
  • Image Ratioing.
  • Albedo Differencing.
pre classification approach1
Pre Classification Approach…..

Composite Analysis

  • Spectral temporal change classification (STCC) by Weismiller et al. (1977).
  • Based on single analysis of a multidate data using pattern recognition and spectral classification.
  • Data sets are collected under similar conditions ( e.g. solar zenith angle) but from different years.
  • A composite data set is created then maximum-likelihood classification performed.
pre classification approach2
Pre Classification Approach…..
  • Advantages:
  • Low cost.
  • Massive data processing.
  • Disadvantages:
  • Optimization of change/no change threshold level.
  • Subsequent interpretation of image difference product.
  • Various Analytical Methods :
  • Image Subtraction and Thresholding.
  • Data Transformation, Subtraction and Thresholding.

Image Differencing

  • Band Ratioing.
  • NDVI.
  • Tasseled-cap transformation.
slide7

Pre Classification Approach…..

Image Differencing ……

Image Subtraction and Thresholding

  • Subtraction of two complementary data sets.
  • Threshold value is assigned to distinguish spectral differences as area of LC changes.
  • Thresholds are based on Standard Deviation value or may derived from the histogram of change image.
slide8

Pre Classification Approach…..

Image Differencing ……

Data Transformation, Subtraction and Thresholding

  • First reduce data dimensionality then data subtraction.
  • Band Ratioing:
  • Calculation of a simple ratio data set.
  • Based on band used, a positive or negative subtraction result can provide valuable insights to changes.
  • Normalized Difference Vegetation Index (NDVI):
  • Most widely used of all vegetation indices.
  • Uses only red and near-infrared portion.
  • Least affected by topographic features compared to numerous other techniques.
  • NDVI = (Near- infrared – Red)/(Near- infrared + Red)
slide9

Pre Classification Approach…..

Image Differencing ……

Data Transformation, Subtraction and Thresholding….

  • Tasseled-Cap Transformation:
  • Enhanced data interpretability by emphasizing the structures in the spectral band .
  • For agricultural crop monitoring out results in three vectors,
  • Corresponding to brightness.
  • Greenness
  • Wetness
slide10

Pre Classification Approach…..

Principal Components Analysis

  • Redundancy reduction technique.
  • The first variable or component contains most variance and succeeding components containing decreasing proportions of data scatter.
  • In a multitemporal data,
  • PC1 and PC2 tend to represent the unchanged land cover.
  • PC3 and later components contain the changed land cover information.
slide11

Pre Classification Approach…..

Change Vector Analysis

  • Defined as the vector difference between the multi-band digital vectors of the pixel on two different dates.
  • The vector describing the direction and magnitude of change from the first to the second date is a spectral change vector.
  • Concept:
  • Two associated one-band images are computed.
  • The first contains the magnitude of the pixel change vector.
  • Second contains the direction of the change vectors.
  • if the magnitude of the change vector exceeds a specified threshold criterion, the decision that change has occurred is made .
slide12

Pre Classification Approach…..

Change Vector Analysis….

  • Once a pixel is identified as having spectral change, the direction image is examined to determine the type of change.
  • The direction of vector contains the information about the type of change.
  • For a given image pixel, magnitude is calculated as Euclidean distance (R) between its location in brightness-greenness space :

Limitation:

  • Sensitive to misregistration, mixed pixel and radiometric differences.
slide13

Pre Classification Approach…..

Spectral Mixture Analysis

  • Based on the premise that multispectral image elements are composed of multiple spectral signatures or “endmembers” that contribute to the overall image reflectance.
  • A linear mixing model is assumed: the total image reflectance can be calculated by the proportion of individual endmember area weighted contributions.
  • Endmembers can be derived from the image data or from lab spectra.
  • Data first converted to reflectance values and that the data be normalized or corrected for atmospheric effects prior to analysis.
  • Major advantage of SMA is to perform spectral unmixing – used to identify subtle LC changes.
slide14

Pre Classification Approach…..

Inner Product Analysis

  • Spectral values of a pixel are considered as multispectral vectors.
  • The difference between the two multispectral vectors is measured as the cosine of the angle between them (cos(a)).
  • Concept:
  • If two multispectral vectors coincide each other, their inner product equal to 1.
  • If some changes took place between two dates of concerned pixel, the inner product would be somewhere between -1 to 1.
  • A one band image is generate to record the inner product.
  • Inner product of two spectral vectors is:

Where x and y are two spectral vectors.

slide15

Pre Classification Approach…..

Correlation Analysis (Spectral Signature Similarity)

  • Conceptually similar to inner product method.
  • The only difference correlation takes into account the means of the multispectral vectors.
  • This helps in reducing effects due to absolute values of two multispectral vectors.
  • Has potential of reducing scene-to-scene radiometric influence induced by differences in total solar irradiance, sun angles, atmosphere effects and sensors.
  • Correlation coefficient for two spectral vectors:

Where x and y are two spectral vectors.

, , takes the value from -1.0 < > 1.0

slide16

Pre Classification Approach…..

Image Ratioing

  • Two co-registered images from different dates with one or more bands in an image are ratioed, band by band.
  • Concept:
  • Areas without significant spectral change will yield similar ratio values.
  • In areas of change, the ratio value would be either higher or lower than the values in the no-change areas.
  • Limitations:
  • Sensitive to misregistration and existence of mixed pixels.
  • To the offsets and gains of the two sensors.
  • Ratio image need extensive human interpretation.
slide17

Pre Classification Approach…..

Albedo Differencing

  • Ratio of the amount of electromagnetic radiance reflected by a body to the mount of incident upon it.
  • Albedo calculated for each pixel is used to create an albedo image (whose DN values are proportional to the albedo).
  • Differencing coregistered image pairs creates quantitative images of the albedo changes.