Automated Building Change Detection using multi-level filter in High Resolution Images ZHEN LIU

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1 Automated uilding Change Detection using multi-level filter in High Resolution Images ZHEN LIU Center of Information & Network Technology, eiing Normal University,00875, Abstract: The Knowledge of urban building change are helpful to urban planning, traffic management and environmental studies. Advances in remote sensing science and the diversity of high resolution data hold great promise for the improvement of more precise information extraction and change detection, making change detection of LUCC at different scales from global to local scales more difficult. However, conventional remote sensing change detection techniques are inefficient due to the high spatial heterogeneity of the spectrum from the surface of an obect, and to greater texture and clearer edges in the image. The requirement for real-time and effective change detection methods calls for the development of more automated techniques of change detection. A method of change detection based on integrating change vector analysis with NDVI filter noise filter,obect-based segmentation, and similarity calculation is presented for high spatial resolution data. It can be used to detect changes in buildings and streets quickly and automatically. The method in extracting and verifying changes in obects is presented, which are illustrated with an airborne linear scanner sensor image over the suburbs of Tokyo, Japan. Using these methods, we detect complex and fuzzy changes in high spatial resolution imagery at different times.the results suggest that change detection based on mulit-leveel filter and obect similarity calibration is more reliable and efficient than post-classification change detection using high spatial resolution imagery. Key words: High Resolution Imagery, Change Detection, Similarity Calibration. Introduction Land use and land cover change very quickly at different scales all over the world nowadays. To maintain the sustainability of geography information systems, real-time and automated change detection techniques are required. In the last 0 years, change detection techniques based on both the airborne and space borne sensors have been widely used in agriculture, urban construction, geo-hazard detection, and national defense. Current change detection methods include the following: difference image analysis, direct classification, post-classification analysis, and comprehensive methods(gong,993, Chen,et.al 00. These methods are based on data promoted in pixel level. As such, they are unable to detect changes at the feature level, and also unable to detect change using automated methods (L 003. Furthermore, with methods based on pixel comparisons, it is hard to determine the structure of the information contained in high resolution remotely sensed images, leading to different change detection results for a given obect. The challenge that the obect-based methods faces is that the detail of ground features are too rich to accurately extract automatically(liu, et.al, 00. This paper focuses on automated change detection for high-resolution airborne photos at two different times. The spatial resolution of the data is 0.m and was acquired using the same sensor, but at different seasons. The target obects for change detection are mainly streets and buildings. We then convert the calculated changes into changed area, possible changed area and non-changed area. An automated change detection method based on obect similarity calibration is proposed in this paper, combining the advantages of pixel comparison and obect-oriented comparison. The results reflect the degree of change of various ground targets at different times. As a result, the method is more suitable for complex ground targets and ambiguous obect changes in high-resolution images.. Data and study area

2 The multi-spectral data were acquired by ADS40 SP in September 00 and April 003. The data has 4 bands, namely NIR, R, G, and, while the resolution is 0.m. The photos cover the suburb of Tokyo. While the flight rout is largely the same, seasonal differences are evident in the data. The classes of the land cover include buildings, roads, forests, and cultivated land. 3. Approach The automated change detection method is based on mulit-level filter and similarity validation, which consists of the following steps(fig.. ImageT ImageT Data preprocessing Generating different images Creating image of change pixels Filtering vegetation change and noise information Extracting changed patch using area filter uilding segmentation in changed area Validating changed building Result output. Fig. Flow chart of automated change detection based on similarity calibration 3.. Data preprocessing The main purpose of data preprocessing is to remove the differences caused by non-surface factors, which includes radiation normalization and geographic registration. Radiation normalization is realized though histogram matching. The geographic registration is achieved by automated calculation. The main steps include the following: automated selection of the candidate template, template searching, similarity calculation, parameter setting, and detection area extraction. Since this paper focuses on change detection, geographic registration will not be described in detail here. 3.. Generating differential images Differential methods, change vector analysis, and principle component analysis can generate differential images (ruzzone, 000. Given the features of the airborne photos, change vector analysis is used to produce the difference images at the two different times the images were taken. Change vector analysis describes the direction and size of the spectral change vector from T to T. Assuming that the pixel gray vectors of the images at the two different times are G( g, g R, NIR g G, g T and H(h NIR, h R, h G, h T respectively. The change vector is ΔG, which contains all the change information between the two images. The change intensity is determined by the following equation Then, Δ G ( g NIR NIR + ( g R R + ( g G G + ( g ΔG is calculated as the pixel value, and the differential image is produced. (,

3 3.3. Creating pixel change images To create the pixel change image, the differential image must be divided into changed pixels and non-change pixels, so that it can be a classification problem (Derrode, 003. A primary problem of unsupervised change detection is that there are not efficient methods to classify the changed pixels and non-changed pixels automatically in the differential image (ruzzone, 000. Creating the changed pixel image can be done by two methods: threshold and classification methods. For automated change detection, the threshold method is a widely used. It uses a threshold to distinguish the pixels in differential image as either changed pixels or non-changed pixels. According to the characteristics of the automated change detection based on similarity validation, the break change method and histogram analysis was used to determine the threshold in this paper. The results indicate that this method can be applied in urban change detection with high-resolution images. In high-resolution images, obects have surface features. When one or many obects change, there will be obvious break points in the relationship curve of the ber of changed pixels to the threshold. Since similarity calibration change detection will determine obect changes and allow for automated validation, the change threshold (T does not insure that the results of the detection are all changed pixels. The change threshold can only distinguish obvious spectral changes, including real ground changes and false changed pixels. T c refers to the changed threshold, while T u refers to the non-changed threshold. The break change threshold should be less than T c, and more than T u but close to the break change point of T u. Histogram analysis is used in this paper to search for the break change point. The relationship between the total ber of the changed pixels P and the change threshold T can be expressed as, P f (T, while the ber of non-changed pixels can be expressed as, P S P. S is the pixel ber of the image. Generally, P P decreases as T and increases. However, after T increases to a threshold value, the change of P and P will appear relatively stable. In contrast, as T decreases, the break change point of P becomes the change threshold. So, the first derivative and second derivative of f (T can be used to determine the change threshold, following which algebraic operations can be used on the differential image to create the pixel change image Extracting changed area Extracting changes in the obect area requires combining the changed pixels and removing the vegetation pixels that undergo seasonal changes, and also the obects that change dynamically such as cars, thus placing an emphasis on extracting changes in buildings and streets. Given the heterogeneity within a single obect, changes in obect pixels image may become cracked, such that the pixels must be combined. If the distance between pixels is less than a given threshold, those pixels should be combined. When filtering the vegetation change information, the degree of vegetation cover at different times is calculated to determine the vegetation pixels. Then, the corresponding pixels in the changed image are set to 0, meaning that those changed pixels are caused by seasonal changes. The degree of vegetation cover can be calculated. Firstly, calculate the NDVI of each pixel according to the formula below NDVI NIR R /( NIR + R ( The degree of vegetation cover for each pixel is: (, ( NDVI NDVI N c ( NDVI + NDVI max min min And then, we calculate the proportion of vegetation pixel in the changed image using (3,

4 N r n m x y N c ( y n* m (4, N c ( y 0 (I N c ( y < N cthreshold (I N c ( y > N cthreshold where N cthreshold is the threshold of the degree of vegetation cover of the vegetation pixels. A pixel whose value is greater than that threshold is defined as a vegetation pixel. If the proportions of vegetation pixels at the two times are both high, the changed pixel reflects seasonal changes of the vegetation, and should therefore be removed. Filtering the changed pixels of dynamic obects caused by match errors, such as, by cars and cracked pixels are done by setting the condition of the area of the speckles. The basic method is to detect the hole in changed speckles using hole detection algorithms and then calculating the area of each speckle Changed uilding Segmentation This approach is based on the algorithm of pixel merging. Defined merging parameter r as r wpδ hp + wtδ ht 0 < w,, following:, p wt wp + wt. The neighboring obects with the least r should be merged in an iterative. r is getting larger and larger in this merging procedure. When r reaches a threshold R defined by user, the merging procedure stops. In the implementation, r is assigned the value from 0 to R, and in an iterative all the neighboring obects with merging parameter equal to r should be merged.the computation process is presented in Fig. Fig.. Changed uilding Segmentation 3.6. Changed uilding Validation In the changed image, many change speckles are caused by radiation, shadow, obect shelter, and seasonal changes of buildings color. A validation method based on the similarity analysis of the image proposed in this paper determines whether the change speckles are real, according to the magnitude of similarity. The method calculates the similarity of the change speckles in original image to estimate the degree of change and to divide the changed image into changed area, potential changed area and non-changed area. Validation ased texture feature

5 A similarity validation based on texture features is proposed in this study to compensate for the disadvantages of the similarity validation based on grads feature. The similarity validation based on the grads feature calculates each pixel according to the grey changes characteristic of each pixel and the adacent pixel; it is suitable for the validation of whole brightness and color changes in a certain region or obect. The similarity validation based on the texture feature better determines whether the inner structure of the same obects at two times has changed or not. The texture feature of the changed area is calculated, following which the similarity of the changed area is obtained using the texture feature value. There are many techniques to compute texture features, though the co-occurrence technique is used in this paper to calculate the texture features. The co-occurrence matrix of the pixels in change area is calculated using P mn m, n, d, α m, n, d, α m n (5, where m, n, d, α refers to the frequency of pixels pairs whose values are respectively m and n; the distance is d and the direction is α. Four directions( , and a x texture window is selected to compute six texture features: contrast, texture variance, co-occurrence and mean, co-occurrence and variance, co-occurrence difference mean, and variance in co-occurrence difference (Chen, Deng and Chong, 00. The result is a texture feature matrix with 4 rows and 6 columns, reflecting the texture feature of each pixel comprehensively. The texture similarity at different times in the changed area is calculated by the formula S ( A( y, ( y 4 i 6 4 i ( T A 6 T * A * T 4 i 6 ( T (6, The similarity of the changed area at different time is expressed as S( A, N M x y S( A( y, ( y M * N (7, where F ( S( A( y, ( y is equal to if the similarity of pixels exceeds a certain threshold (e.g. 0.5, but is otherwise equal to 0. Using the computation of similarity of the changed area, the magnitude of the similarity can be used to determine the change intensity of the changed area and whether real change occurred. If the similarity is greater than a big threshold, that area did not change. If the similarity is less than a small threshold, the area changed. If the similarity is between the two thresholds, the area is potentially changed area, and needs to be validated by field studies. Validation ased on Portion Hausdorff Match The Hausdorff distance is a measure of the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two obects that are superimposed onto one another(huttenlocher, 993. A Voronoi surface, d( of a set, is also referred to as a distance transformer because it gives the distance from any point x to the nearest point in a set of source points,. Calculating in this way will cause the gray level value of Voronoi surface image to be scaled 5 times greater. For each candidate changed building, one uses the silhouette as a mask and puts the mask on the Voronoi surface. For a mask that has N pixels, if n of the corresponding pixels in the Voronoi surface is smaller than the Hausdorff distance threshold, then the similarity S n/n. If the similarity is greater than a given threshold, then the corresponding building is treated as one that has not changed.

6 4. Results and Discussion An image of sq km was used. The results indicate that the detection change in high-resolution images based on the validation of change speckles is an efficient approach. The pixel change image made by change vector analysis and by automated change threshold reflects the real changes in the land surface.the result shows that changed pixels contain much change information, such as, the change of buildings, seasonal changes of vegetation, shadows, and dynamic obects. After extracting the change obects and filtering vegetation change pixels, dynamic obects, and cracked images resulting from matching errors, we obtain the changed area of buildings and streets. The second changed area was caused by housebreakings, while the first and third changed areas were caused by shadows. The fifth changed area was a house sheltered by trees. The similarity calibration for these changed areas showed that there were high similarity changed areas labeled with yellow or green rectangles caused by differences in illumination and shadows (See Fig 3. The changed area labeled with red rectangles means that the inner structure of the area was changed. The similarity based on hausdorff distance between two edges of building segmentation was more Fig.3 The Result of Changed uilding Validation suitable for measuring the change of inner structure of the area, especially changes after houses collapse as a result of earthquakes. As for the changed area caused by illumination and shadow differences, both similarity validations obtained the desired results. The result of the similarity validation for the changed area implied that the magnitude of the similarity is positively correlated with the real change of the land surface; the effect of the response to the type of change is different, providing a reference for the selection of changed obects. Conclusion For change detection using high-resolution images, a mulit-level filter-based approach has robust noise resistance compared with a classification method. Since the spectral differences of the land surface is evident in high-resolution images as well as the effect of all kinds of noise, the change information obtained is much greater than in low-resolution images; but, most changes are false changes. ase on the method proposed in this paper, the noise can be removed to a

7 certain degree. Moreover, the noise filter and extraction of changed obects decrease the change speckles of dynamic obects, and also the matching errors to a certain extent. In high-resolution images, texture information can be used to identify the changed area and changed property. The texture feature is much richer and is not easily affected by imaging conditions. It is more robust compared to spectral features. This paper provides a quick and effective method for change detection in high-resolution images. However, the image diversity caused by sensor and flight routes will influence the detection results, so in order to improve the accuracy of change detection, more auxiliary information such as DSM is needed. References: ruzzone L, Prieto D F Automatic Analysis of the Difference Image for Unsupervised Change Detection[J]. IEEE Transactions on Geoscience and Remote Sensing. 38(3, pp Chen Jin, He Chunyang, Shi Peiun et.al. 00. Land Use/Cover Change Detection with Change Vector Analysis(CVA: Change Magnitude Threshold Determination[J]. Journal of Remote Sensing. 5(4, pp Chen Zhipeng, Deng Peng, Chong Jinsong et.al. 00. Application of Textural Features to Change Detection in SAR Image[J]. Remote Sensing Technology and Application. 7(3, pp Derrode S, Mercier G, and Pieczynski W Unsupervised Change Detection in SAR Images Using a Multicomponent HMC Model[C]. Multi-Temp 03, July 6-8, Ispra, Italy. Gong P Change Detection Using Principal Component Analysis and Fuzzy Set Theory[J]. Canadian Journal of Remote Sensing. 9(, pp. -9. Huttenlocher, D. P., Klanderman, G. A., and Rucklidge, W. J Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 5, 9, Li Deren Change Detection Using Remote Sensing Imagery [J]. Journey of WuHan University (Information Science. 8(S5, pp. 7-. Liu Z., Gong P., Shi PJ., et.al.. Automated uilding Change Detection Using Ultracamd Images and Existing CAD Data. International Journal of Remote Sensing, 00,vol 3(06, pp