GRAPH MATCHING BASED CHANGE DETECTION IN SATELLITE IMAGES

Murat Ilsever and Cem Unsalan

Computer Vision Research LaboratoryDepartment of Electrical and Electronics Engineering

Yeditepe University

Istanbul, 34755 TURKEY

ABSTRACTChange detection from bitemporal satellite images (taken

from the same region in different times) may be used in

various applications such as forest monitoring, earthquake

damage assessment, and unlawful occupation. There are

various approaches, based on different principles, to detect

changes from satellite images. In this study, we propose

a novel change detection method based on structure infor-

mation. Therefore, our method can be called as structural

change detection. To summarize the structure in an image,

we benefit from local features and their graph based rep-

resentation. Extracting the structure from both images, we

benefit from graph matching to detect changes. We tested our

method on 18 Ikonos image pairs and discuss its strengths

and weaknesses.

1. INTRODUCTION

Change detection from bitemporal satellite images may be

used to solve various remote sensing problems. The most

important of these are earthquake damage assessment, for-

est monitoring, and detecting unlawful occupation. To detect

changes from bitemporal images, various methods are pro-

posed in the literature. These can be broadly grouped as pixel,

texture, spectrum, and structure based methods. There are ex-

cellent review papers on these topics [1, 2, 3].

Initial change detection studies mostly focused on pixel

based methods. Recent satellite images have sufficient reso-

lution such that, object details can be observed. Therefore, de-

tailed change detection can be performed on them. Thomas etal. [4] benefit from this detailed information to detect hurri-

cane damage detection. In our previous studies, we benefit

from graph theory and local feature representations to grade

changes [5, 6]. In this study, our focus is detecting changes

using local features in a graph formalism. To represent the

structure, we extract local features from both images. Then,

we represent each local feature set (extracted from different

images) in a graph formation separately. This allows us to

detect changes using graph matching.

This work is supported by TUBITAK under project no 110E302.

2. LOCAL FEATURE EXTRACTION AND GRAPHREPRESENTATION

This section summarizes how we represent the structure in

the image. In the following section, we will benefit from this

representation to detect changes in bitemporal images.

2.1. Local Feature Extraction

We pick the features from accelerated segment test (FAST)

method to detect local features in images in a fast and reliable

manner [7]. This method can briefly be explained as follows.

For each candidate pixel, its 16 neighbors are checked. If

there exist nine contiguous pixels passing a set of tests, the

candidate pixel is labeled as a local feature. These tests are

done using machine learning techniques to speed up the op-

eration. We used FAST based local features in our previous

studies and obtained good results [8]. Therefore, we also use

them in this study.

2.2. Graph Representation

To extract the structure information from local features, we

represent them in a graph form. A graph G is represented

as G = (V,E), where V is the vertex set and E is the edge

matrix showing the relations between these vertices. Here

vertices are local features extracted by FAST. The edges are

formed between them just by their distance. If the distance

between the two vertices are small, there will be an edge be-

tween them. In this study, we set this difference value to 10

pixels depending on the characteristics of the objects in the

image.

3. DETECTING CHANGES BY GRAPH MATCHING

As we form graphs from both images separately, we apply

graph matching between them. In matching graphs, we ap-

ply constraints both in spatial domain and in neighborhood.

We can summarize this method as follows. Let the graph

formed from the first and second images be represented as

G1(V1, E1) and G2(V2, E2). In these representations, V1 =

6213978-1-4673-1159-5/12/$31.00 ©2012 IEEE IGARSS 2012

{f1, ..., fn} holds the local features from the first image and

V2 = {g1, ..., gm} holds the local features from the second

image. We first take spatial constraints in graph matching.

We assume that two vertices match if the spatial distance be-

tween them is smaller than a threshold. In other saying, fi

and gj are said to be matched if ||fi − gj || < δ, δ being a

threshold. This threshold adds a tolerance to possible image

registration errors. Non-matched vertices from both graphs

represent possible changed objects (represented by their lo-

cal features). We can also add neighborhood information to

graph matching. To do so, we first eliminate vertices having

neighbors less than a number. Then, we match these refined

vertices. This way, we eliminate some local features having

no neighbors (possible noise regions).

4. EXPERIMENTS

We tested our method on 18 registered Ikonos image sets ob-

tained from different regions of Turkey. They hold a total of

717 changed objects. In this section, we provide a sample

change detection result. We also provide the quantitative ob-

ject based change detection results.

4.1. A Sample Test Result

We provide a sample test image set from Adana region. We

provide the local features extracted from both images in

Fig. 1(a). We also provide the change detection results with

using only spatial constraints in Fig. 1(b) and three neighbor-

hood constraints in Fig. 1(c). As can be seen, non-matched

local features indicate the changed objects.

(a) Extracted local features for the test image set

(b) Using only spatial constraints (c) Using three neighborhood

constraint

Fig. 1. The Adana test image set and change detection results.

4.2. Object based Change Detection Results

We finally quantify object based change detection results

in this section. We benefit from two previously introduced

performance criteria as Detection Performance (DP) and

Branching Factor (BF) defined in [9]. These criteria are

defined as

DP =(

TP

TP + FN

)(1)

BF =(

FP

TP + FP

)(2)

where TP is the number of truly detected changed objects in

the ground truth image. Changed objects are assumed to be

truly detected if any object in the resulting image overlaps the

ground truth object. FN is the number of changed objects in

the ground truth image which are not detected. FP refers to

the extra object labels. For DP , we obtain these numbers in

terms of objects. However, for BF they can only be calcu-

lated in terms of extracted local features.

The performance results are as follows. Over 717 changed

objects, we obtain TP = 432 and FN = 285. Therefore, we

obtain the detection performance as DP = 0.6025. For the

branching factor, we obtain BF = 0.4614. These results are

promising taking into account the object level complexity and

image types in our test set.

5. CONCLUSIONS

In this study, we propose a novel change detection method

based on local features and graph matching. This method can

detect object based changes. The idea behind the method is as

follows. From each image, local feature points are extracted.

They are represented by two separate graphs. These graphs

are matched to eliminate similar local features. While match-

ing local features, neighborhood constraints are imposed

on them. Non-matched feature locations represent possible

changed regions. We tested our method on 18 sets of Ikonos

images and obtained promising results. This study may be

extended further by adding segmentation information. Then,

the local features labeled as change can be used to select

changed segments as well.

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