Change

Detection is the

assessment

of changes

in the type

or condition

of surface

features.

Whether the unit of analysis is a pixel, a neighborhood, a

multitemporal segment, or even (rarely) a spectral class, it is very

important to follow the image preprocessing steps that minimize

signal from variation that could be confused with the change

detection signal of interest. Ideally, this means that the images being

compared be:

1. Acquired from the same or well intercalibrated sensors and

acquired at the same time of day using the same IFOV and

look angle.

2. For interannual analyses, acquired during the same season

to minimize differences due to phenological changes.

3. Well coregistered, preferably to within two-tenths of a pixel

or less (Dai and Khorram 1999).

4. Free of clouds in the area of analysis.

5. Corrected to top-of-atmosphere or (preferably) surface

reflectance (see Chapter 11).

6. Free of other conditions not deemed part of the signal of

interest.

Bitemporal Change Detection

These analyses use images

acquired at two points in time.

Visual Interpretation-simple visual comparison of images from two dates

Image Algebra-arithmetic operations are applied to corresponding pixels in

each image

Transformation/Data Reduction-the data in the original image can be

trans- formed to new axes composed of linear combinations of the existing

bands (example PCA).

Classification-spectral change detection that could be very powerful.

Statistical Techniques-numerous ways to compare images statistically.

A series of powerful earthquakes rattled northern Japan starting on the evening of October 23. This IKONOS image shows a massive landslide over the EnokiTunnel.

Haiti Earthquake

Dry summer with low flows (July 1988). The river during the disastrous flood of July 1993.

Where the Missouri River joins the Mississippi River at St. Louis, Missouri.

Multi-temporal Change Detection

These analyses use images from

sensors more widely available

and produce time series.No-Cost Data Enabling New Class of Analyses-the availability of “free”

data is opening up a rich set of applications never before possible, as

analysts can now use multitemporal stacks of images, be they of original or

derived bands, to conduct their analyses. Ex. EarthExplorer

Tradeoffs-Cloud cover, images need to be geometrically and radiometrically

calibrated, errors in reflectance, temporal changes in the signal, high data

volume, algorithms are still under development.

Pre-processing-different alternatives must be considered (check book).

Time-Series Analysis-a sequence of data points evenly sampled through

time.

AERIAL PHOTOGRAPHY TO

DETERMINE TEMPORAL CHANGES

La Parguera in 1936 La Parguera in the 80's

La Parguera in 2010

Postclassification

Change Detection

Comparing images subsequent to

classifying each. Consists only of

comparing the “from” class and

“to” class for each pixel or

segment.