Change Detection

The process of identifying differences in the state of an object or phenomenon by observing it at different times.

Change detection applications: 1. Deforestation assessment, 2. Vegetation phenology 3. Urban expansion 4. Damage assessment 5. Crop stress detection 6. Snow melting …

Change Criteria Before a change detection technique is applied, the analyst has to define what is considered to be a change.

Change Detection Techniques

1. Univariate image differencing The most straight forward way to see whether a change has happened is to take a difference between two images collected over the same place at different times.

)()( 12 tDNtDND ijijij

The variable to use can be NDVI for vegetation change detection, or a single band reflectance (Skole and Tucker (1993) used TM 5 only to detect deforestation in Amazon). This difference is done pixel by pixel. Therefore, a high standard is required. If geometric correction between the two images is poor, spurious change may be identified. Theoretically, if D deviates from zero, a change has happened. In practice, this may not be true. There may be a systematic shift as where the threshold value is between change an stable pixels. Such systematic shift may be caused by sensor degradation, atmospheric conditions, phenology etc. Determining the threshold for change and non-change is the critical step for this technique.

Disadvantage: do not know what has changed (from-to)

Image Ratioing

Image ratioing for change detection is based on the following fact that the ratio of the DN values for a stable feature over two dates would be unity, while changed pixels would have a ratio significantly different from unity.

)(

)(

1

2

tDN

tDNR

ij

ijij

However, due to various external factors, the stable features may not have an expected value of unity from the ratio. A critical step is to determine the threshold value to differentiate change/no-change, which is often empirical.

No from-to information on change detection is available.

Image regression

In this method of change detection, the pixels values at date 1 is assumed to be linearly related to those at date 2. Therefore, we can built a linear relationship between the images in date 1 and date 2. We can predict the pixel values from date 1 to date 2. Take a difference between the actual date 2 value with the predicted date 2 value, a change/no-change map can be generated from the difference image by thresholding.

Date 1

Date 2

Change Vector Analysis

The reflectance values collected for a pixel can be thought of as coordinates for a vector in multidimensional space. The vector difference between two dates indicated the change for a pixel.

Band x

Band y

Date 1

Date 2Change vector

Change can be measured in two ways, the magnitude of change (the modulus of the change vector) and the direction of change (the angle between the two vectors).

Change Vector Analysis

Change vector analysis cannot differentiate the following difference

Band x

Band y

No from-to information is available from change vector analysis, but you can differentiate different types of changes based on the direction of change vectors

Post-Classification Comparison for Change Detection

It is also straight forward that we classify the images collected over the same place at two times first and compare the classified maps pixel by pixel to identify changes.

Date 1 class map

Date 2 class mapChange detection map

Advantages of Post-Classification change Detection:

1. Because the classes before and after the change are known, we can construct a change detection matrix showing a from-to change.

Date 1 image

Date 2

C1C2C3…Cn

C1 C2 C3 … Cn

Diagonal elements are stable areas, off-diagonal elements are changes. 2. Simple to understand and easy to apply

Major disadvantage: Classification errors accumulate from two separate classifications. For example: If the classification accuracy is 80% at each classification step, the change map can not be more than 64%, not accounting errors caused by misregistration.

Change Detection

Use of Multitemporal NDVI features:

perennials

Barren fields

Reduced productivity fields

Productive fields

Maximum NDVI

NDVI Range

Color composite of three NDVI images

Colored Areas: ChangesGrey areas Stable

For this particular image does not show much changes in NDVI values because the forests in these areas are evergreen. Thus the image is mostly gray. The areas with clout and snow does show some change in color.

We also see some changes in the lower part of the image.

RGB=NDVI at May 20, Jun 30, Aug 17, 2002

Multidate Composite Analysis

This method is developed to overcome the shortcoming in post-classification comparison change detection where the change map accuracy is often lower than the individual maps. This method put multidate images together as if they were a single date image

Image of date 1 n bands

Image of date 2 n bands

Composite image 2n bands

Change Detection with Multidate Composite Image

1. Direct multidate classification

Classify (either supervised or unsupervised) the composite image as if it were a single date image. The change feature will become a distinct class. The advantage of this method is that only one classification is involved, thus a higher accuracy of change map.

2. Principal component analysis

A principal component analysis may lead to a component that primary containing change information between two dates. This method can also reduce noise effect on change detection as some noise, like atmospheric effect, may be compressed into a separate component.

Multitemporal Kauth-Thomas Transformation (Collins and Woodcock, 1996 RSE 56:66-77)

KK

KKM

2

2

Assuming K is the matrix containing the KT transformation, then the following matrix M is the Multitemporal Kauth-Thomas transformation (MKT).

This transformation leads to orthogonal components, meaning there is no redundancy in information content. We already know that KT transformation leads to orthogonal components. Thus we can tree K as an matrix component and test if the column vectors in the above matrix are orthogonal to each other.

The Standard to Test Orthogonal Vectors

Let

nv

v

V ...1

nu

u

U ...1

The vector V and U are orthogonal to each other if and only if the dot product of the two vectors are zero, i.e.

0...... ... 11

1

1

nn

n

nT uvuv

u

u

vvUV

Test for Orthogonality for matrix M for MKT transformation

Let

K

KV

K

KU

0 22

KK

K

KKKUV T

To maintain the transformed data on the same scale as the original data, the transformation vectors should be a unit vectors. K is already a unit vector (How can you test that?). The length of the column vector for M is:

211)()( 22 KKU

211)()( 22 KKV

To normalize the vector in M to be unit vector, we divide the matrix by 2