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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
ACCURACY IMPROVEMENT OF CHANGE DETECTION
BASED ON COLOR ANALYSIS
J. Wang, H. Koizumi, T. Kamiya
System Technologies Laboratories, NEC System Technologies, Ltd, 6300212, Ikoma, Japan - (wang-jxb, koizumi-hxa,
kamiya-txa)@necst.nec.co.jp
Technical Commission VII/5
KEY WORDS: Change Detection, Orthoimage, Color Analysis, Illumination Change Adjustment, Location Difference
Rectification
ABSTRACT:
Change detection is one of the most important topics in the application field of aerial images and satellite images. In this paper, a
novel framework for accuracy improvement on change detection is proposed. The proposed framework consists of three parts. Firstly,
the location difference between the two orthoimages for change detection is rectified globally. Secondly, the illumination change of
the orthoimages from two different times is adjusted to produce the orthoimages with more unified illumination condition. Thirdly,
the location difference between the two orthoimages is adjusted in more precise level of local regions. Experimental results show that
the detection accuracy of change detection is greatly improved through the pre-processing of the proposed framework.
1. INTRODUCTION
As one important application field of aerial images and satellite
images, change detection is attracting more and more attentions
in recent years. Due to the fast development of cities especially
in the developing countries, there are new reconstructions,
demolished old buildings, vacant land changing into grassland
and so on, nearly happening everyday. This leads to dramatic
land change in a short time. In order to timely update the map of
these changing places in wide area, change detection with high
accuracy on aerial images or satellite images is quite necessary.
Before, this updating job is realized by manual check of human
operators, but it turns out to take very long working time and
there also exist changing regions missed by human operators.
Under this background, several change detection systems have
been developed to automatically carry out the change detection
on aerial images and satellite images. For example, we can
extract the color change and the height change based on
orthoimages and Digital Surface Model(DSM), which are
generated based on stereo images and aerial triangulation data.
The real application projects prove that automatic change
detection systems help to greatly reduce the overall processing
time, and further the overall cost. At the same time, automatic
systems also improve the overall detection rate, by extracting
the regions apt to be missed by human operators.
To assure that no changing region is missed in the final result,
automatic change detection systems tend to firstly extract much
larger number of candidate regions far more than the number of
actually changing regions, which needs further processing by
the operators to remove false detections. Though the overall
processing time is already greatly reduced, operators still have
to spend long time to remove wrong detections. To solve this
problem, we propose a framework to reduce most of the wrong
detections and at the same time keep the correct detections. The
experimental results show that through the processing steps in
the proposed framework, the number of wrong detections is
greatly reduced and the location and range of detected regions
are also improved.
The rest of the paper is organized as follows. Section 2 explains
the research background for the improvement of change
detection. In section 3, the details of the whole methodology in
three steps are stated. Experimental results are shown and
analyzed in section 4. Finally, we draw a conclusion and also
introduce future prospects in section 5.
2. RESEARCH BACKGROUND
Due to its wide application, many change detection methods [1,
2, 3] have been proposed. In comparison, there is no systematic
discussion on how to improve the accuracy of change detection
by resolving some common problems in almost all change
detection methods. In this paper, we concentrate on analyzing
the problems that affect the accuracy of change detection.
Firstly, the necessary requirements for a change detection
system are discussed as follows. (1) Completeness. No changing
region is missed no matter of its scale, shape and changing
pattern. (2) Correctness. No unchanging regions are extracted.
(3) Preciseness. The location and the range of the extracted
changing regions should conform to the truth.
According to these requirements, current automatic change
detection systems have relatively good completeness. But to
assure the completeness, some suspectable but actually
unchanging regions are also detected. Through the analysis of
several different projects, we find the following two main
reasons for the wrongly detected regions: illumination change
between two orthoimages and location difference between two
datasets. At the same time, there exist incomplete candidate
regions because of illumination change and location difference.
We propose a new framework to solve the above two main
problems, and finally find that the preciseness of the detected
regions is also improved at the same time.
