Interactive Segmentation for Change Detection In Multispectral Remote-Sensing Images

munchsistersAI and Robotics

Oct 17, 2013 (3 years and 7 months ago)

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Interactive Segmentation for Change Detection

In Multispectral Remote
-
Sensing Images


Abstract


In this letter, we propose to solve the change detection

(CD) problem in
multitemporal remote
-
sensing images using interactive

segmentation methods. The user
needs to input markers

related to change and no
-
change classes in the difference image.

Then, the pixels under these markers are used by the support

vector machine classifier to
generate a spectral
-
change map. To

enhance further the result, we include the
spatial
contextual information

in the decision process using two different solutions

based on
Markov random field and level
-
set methods. While the

former is a region
-
driven method,
the latter exploits both region

and contour for performing the segmentation

task.
Experiments

conducted on a set of four real remote
-
sensing images acquired by

low as well
as very high spatial resolution sensors and referring to

different kinds of changes confirm
the attractive capabilities of the

proposed methods in generating a
ccurate CD maps with
simple

and minimal interaction.













Existing System


The user inputs could be valuable in steering the segmentation process in order to obtain
accurate results. Usually, interactive
-
based segmentation methods start by exploiting the user
inputs through a set of strokes, lines, scribbles, or curves for genera
ting labeled pixels for object
and background termed as
seeds
. Then, on the basis of these seeds, the segmentation process is
carried out using, for example, adaptive weight distances spline regression and maximal
-
similarity
-
based region merging. Obviously
, the more user interactions we have, the more
accurate is the result, but ideally, the level of interaction is usually referred to be simple and
minimal. In remote sensing, user
-
based interaction methods have been developed to address
supervised classific
ation problems. The basic idea of these semiautomatic methods known as
“active learning” is that, starting from a small and suboptimal training set, additional samples,
considered important, are selected in some way from a large amount of unlabeled data (l
earning
set). Then, these samples are labeled by the user and then added to the training set. The entire
procedure is repeated until a stopping criterion is satisfied.



Disadvantages




Then, the pixels under these markers are used for training a support v
ector machine
(SVM) classifier in a similar way to supervised remote
-
sensing image classification.




After training, the pixels in the image are initially classified with SVM as change and no
change. It is a well
-
known fact that the analysis of image pixels

under spatial
independence assumption may lead to inconsistencies due to several reasons, which
include, for example, the co registration noise.








Proposed System

Depending on the initialization of the LS function, the minimization

of the energy
functional of the original CV model

could be easily trapped into a local minimum. In particular,

this risk is increased when the DI is corrupted by noise due

to co registration errors. To reduce
this effect, an automatic

multiresolution approach termed as
MLS. Its

basic idea is to analyze the
DI at different resolutions, namely,

from course to fine resolutions by successively down
sampling

the image with a factor of two. The spatial down sampling of

the DI leads to interesting
properties as it provides a le
ss noisy

image and reduces the search space and the number of local

minima. The combination of SVM and MLS consists in setting

the initial contour
φ
0 as the SVM
change map then running the

MLS algorithm the sake of comparison, we provide also in Table I
th
e

results of two state
-
of
-
the
-
art unsupervised CD methods. The

first is based on the fusion of
an ensemble of different thresholding

algorithms through MRF. The second is the standard

MLS
method developed where the initial curve is set as

small rectangles
uniformly covering the entire
DI, so that the

likelihood of capturing the changed regions can be maximized,

which may occur
at different positions of the image.

Advantages



The help of these markers, the method calculates the similarity of different
regions and merges them according to a maximal
-
similarity rule. In particular, the
authors adopted the Bhattacharyya coefficient to measure the similarity between
these regions.



The latter is based on an initial partitioning of the image into homogenous re
gions
using the mean
-
shift algorithm. Then, the user introduces label information via
interactive line markers.



The results of a recent interactive segmentation

method based on maximal
-
similarity region merging (MSRM)
.





System Configuration


H/W System
Configuration:
-

Processor


Intel core2 Duo

Speed
-

2.93 GHz

RAM


2GB RAM

Hard Disk
-

500 GB

Key Board
-

Standard Windows Keyboard

Mouse
-

Two or Three Button Mouse

Monitor


LED


S/W System Configuration:
-


Operating System: XP and windows 7


Front End: MATLAB



Module Description


Support vector machine

Then, the pixels under these markers
are

used

for training a support vector machine
(SVM) classifier

in a similar way to supervised remote
-
sensing image classification.

After
training, the
pixels in the image are initially

classified with SVM as change and no change. It is a
well
-
known

fact that the analysis of image pixels under spatial

independence assumption may
lead to inconsistencies due to

several reasons, which include, for example, t
he
co registration

noise.


Pixel Analysis with SVM

The latter is among the most popular

supervised kernel
-
based classifiers available in the
literature.

Compared to standard classification methods, it relies on the

margin maximization
principle that makes
it less sensitive to

over fitting problems
. During the classification stage, a

spectral
-
change map is generated by classifying the remaining

pixels in the DI as change or no
change.


Data Set Description

The effectiveness of the proposed CD method,

different multitemporal remote
-
sensing
images acquired by low

as well as very high spatial resolution (VHR) multispectral

remote
-
sensing sensors and referring to different kinds of

changes were used in the experiments.


Thresholding algorithms



The first
is based on the fusion of an ensemble of different thresholding algorithms
through MRF. The second is the standard MLS method developed in where the initial curve is
set as small rectangles uniformly covering the entire DI, so that the likelihood of captur
ing the
changed regions can be maximized, which may occur at different positions of the image. In
addition, we include the results of a recent interactive segmentation method based on maximal
-
similarity region merging (MSRM).



CONCLUSION


This letter has
presented two interactive segmentation methods

for solving the problem of CD in
remote
-
sensing images.

The first is based on the combination of SVM and MRF, while

the
second combines the SVM and LS methods. The experimental

results obtained on four differe
nt
multitemporal remote sensing

data sets have shown that the proposed approach has the

following
characteristics: 1) It is very attractive in generating

accurate CD results with minimum
interaction, and 2) it is

robust against initial markings compared to

the interactive

MSRM
method. The latter fails when confronted with images

characterized by changes situated in
different regions of the

DI. Future development of this approach is to extend it to the

multichannel case that may characterize the DI.




















REFERENCES

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change
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461, Oct. 2006.



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