Dynamic monitor on urban expansion based on a object-oriented approach
Chunyang He; Jing Li; *JinShui Zhang;Yaozhong Pan; YunHao Chen
Key Laboratory of Environmental Change and Natural Disaster,
Ministry of Education of China, Beijing Normal University;
College of Resources Science & Technology,
Beijing Normal University, the People’s Republic of China, 100875
* Corresponding author: email@example.com
Abstract—In this paper, a new object-based change detection encroachment from 1937 to 2003 in southern New Mexico . approach is developed. The approach consists of three steps: (1) L. Bruzzone brought an adaptive parcel-based technique for producing multi-scale objects from multi-temporal remote unsupervised change detection. Ola Hall take a multiscale sensing images by combining the spectrum, texture and context object-specific approach to digital change detection. All of information; (2) extracting potential change object by the above have made some success in change detection with comparison of the attributes of shape, structure, texture, etc. of objected-oriented change detection approach , whereas such each object; (3) determining the changed object and detecting application needs prior knowledge that leads to error urban expansion area with the help of in-situ investigation. When accumulation, or average spectral information as object unit the object-based approach was applied to the urban expansion that destroy the spatial information. detection in Haidian District, Beijing, China with the support of
two Landsat Thematic Mapper (TM) data in 1997 and 2004, the Given the shortcoming of traditional land use/cover change satisfactory results were obtained. The overall accuracy is about detection and object-oriented approach, which are influenced 80.3%, Kappa about 0.607, which are more accurate than post-by the sensor and weather. In this paper, making full use of the classification change detection. The newly developed object-stability of spatial object texture between two temporal based change detection approach possesses the advantage of its remotely sensed data, a new urban change detection approach reduction to error accumulation of image classification of that computes the object texture similarity between the two individual date and its independence to the radiometric image at different is developed to extract the urban change correction to some extent. information in order to obtain the urban/non-urban changed
information accurately, which makes up for the shortcoming Keywords- change detection; object-orient; similarity; remote based on post-classification that produces the error sensing; texture; land use/cover accumulation and the present object-oriented approach that
destroys the spatial information.
II. OBJECT-ORIENTED CHANGE DETECTION Timely and accurate change detection of earth’s surface
features is extremely important for understanding relationships Object, which is extracted from remotely sensed images, is and interactions between human and natural phenomena in the pixel collection that contains spectral, spatial properties. order to promote better decision making . Urban is the most Let us consider two co-registered multispectral images, X1 and sensitive in land use/cover change. To obtain the urban land X2, acquired in the same area at two different times, t1 and t2. use/cover change information is important for urban decision-Objects A and B are extracted from the X1 and X2, respectively. making and sustainable development. The current change Let C be defined the shared object of the two images, which detection approach with remotely sensed data can be generally satisfies the following conditions: grouped as two types of spectral classification-based approach
and pixel-by-pixel radiometric comparison approach. The (1) CAB？
former has the obvious limitation for its cumulative error in tt12 (3) image classification of an individual date and the latter needs SRR？CCthe strict radiometric correction, which is becoming the t1t1t1t1t1R = (f;f…;f…f) is object A’ the property obstacle for their wide application in current urban expansion Ai1i2iviRt1detection. collection, where f is the object A’ v property, such as ivt2t2t2t2t2 Objected-oriented change detection extracts the change spectral, texture and etc. R= (f;f…;f…f) is Bi1i2iviRinformation from the two temporal remotely sensed data based t2object B’ the property collection, where f is the object B’ v ivon object unit using the texture, structure and etc. There are property. S is the relationship of the entire object C’ features some studies on the objected-oriented change detection.
which is shared by the two images. S will determine whether Ecological theory, in particular hierarchy theory, predicts that changes in landscape spatial pattern and temporal scales at the object C will be changed or not at some rules. which they are assessed . Volker Walter segments the image
using GIS database to obtain the change information .
Andreas S. Laliberte takes analysis for mapping shrub
The research is supported by National Hi-Tech Research and Development Program of China, (No. 2003AA131080)
property of the same land type possess stabilization without III. FLOW OF OBJECT-ORIENTED URBAN CHANGE DETECTION
changing with times, which is usually to be adopted in change The approach of object-oriented urban change detection analysis about images at different times. The texture property is computes the two co-registered images’ shared object related to the pixel window size, and texture is extracted at similarity in order to determine whether the object has changed different pixel window from different resolution images. So or not based on the multiscale object unit. Flow chart is experiments are necessary to make certain the optimum illustrated in Fig. 1 window size in order to extract the texture information.
Data Pre-ProcessingD. Compute potential change objects’ similarity
After step (3) and (4), the objects’ similarities are computed
by use of the potential objects’ properties including texture and
structure. There are two kinds of modes to compute objects’ Unban/Non-unbanTexture Extractionsimilarity: Distance method and correlation coefficient. object extraction
(a) Distance method Similarity is computed by use of
pixels’ gray value, statistical value and property value. Potential ChangedTexture Extraction WithGenerally. Distance is long, while the similarity is little. Objects ExtractionOptimum Window SizeThe method of absolute minus, average absolute minus and
square minus are usually used during the distance
Object's Similarity(b) Correlation method Similarity is computed by use of Computationtwo images vector’s angles. The normalized multiplied
correlation and correlation coefficient method are usually used.
Let Xij belong to object A, while Yij belong to object B, then Changed Objectthe similarity can depicted as follow.
Extraction X*Y？？ijijij(3) Figure 1. Flow Chat of Object-Oriented Change Detection ？S(A,B)22X*Y？？？？ijijiijj A. Extract the urban/non urban objects
Xij and Yij are the gray value of the two images, Multiscale objects are extracted from the two images at respectively, or some statistical value such as values of the different times according to the spectrum, texture, structure and mean, variance peak and etc, or the single pixel’s change context. In this study, we focus on urban and non-urban land property value such as texture value, grades value and blur type. Some land types such as water, vegetation are merged property value. into non-urban, while others are merged into urban. The
urban/non urban objects will be extracted from the two images The objects’ similarity is computed by use of spatial by use of object-oriented supervised classification method. information, which are not same when the indices are different.
Let us define the optimum spatial information indices that can B. Extract the potential change objects separate the change/non-change object adequately. There are two parts in urban change detection including The segregative degree of change/non-change objects, urban changing into non-urban and non-urban changing into which are described as follow, is defined in order to separate urban. From the first step, make the two result images from the changed/unchanged objects. which the urban/non-urban objects are extracted by transfer
image operation. There are four changed land type: urban to ||MeanMean；cuc(4) fcuc(,)？urban, non-urban to non-urban, urban to non-urban, non-urban VarVar;to urban. The last two land change types are defined as the cuc
potential changed land type, while the former two change land fcuc(,)is the segregative degree of changed/unchanged types, which are classified accurately, are considered as objects. Mean and Mean is the mean value of change/non-cucunchanged land type. The potential changed objects are change objects’ similarity collection, respectively. Var and cemphases during the urban object-oriented change detection. Varuc are variance values of the change/unchanged objects’
fcuc(,)similarities’ variance. The higher of is；the more C. Extract the texture information with the optimum window different between changed/unchanged object is, which can size distinguish the changed object from non-changed object more Texture property that is the pixel frequency of the images is fcuc(,)effectively, otherwise the is lower, which can’t the synthesis of the objects’ shape, size, shadow and hue, and distinguish them effectively. reflects the local pixels’ gray value and hue rules. The texture
E. set the threshold to extract the changed objects C. Extract the potential changed objects
The difference of texture between urban and non-urban is Overlapping the maps by use of the map algebra produces high, so the texture properties change a lot during the process four land types, including urban, urban to non-urban, non-of urban to non-urban or non-urban to urban. The changed urban to urban, non-urban. The non-urban to urban and urban objects that are extracted satisfy the follow: to non-urban are looked upon as the potential changed objects.
1 (S > T)