Document Type : Research Paper


1 PhD Student in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran

2 Assistant Professor in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran


Extended Abstract
Change detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can face a different challenge; most of the CD method from a high-resolution image requires training procedure to overcome this challenge. In this paper, an unsupervised (without needing training process) CD algorithm proposed from the high-resolution image. In this method spatial and spectral features extracted from bi-temporal images of the studied area. Difference images generated from high information content features. Then generated different images mapped into spherical space. The Primary change map created using implemented multi-thresholding method on created spherical space and the second change map created using hierarchical clustering regularized by Markov random field method. The final change map created by integrating the result of primary and secondary change maps. The final change map shows an overall accuracy of 92.56% in the studied area.
Data and methods
The data used in this paper is a subset of the main data with dimensions of 2000 * 2000 from an urban area in the city of Mashhad. These images corresponded to the two periods of 1390 and 1395 and were taken with UAV. The orthoimage is related to the first time with a spatial resolution of 6 cm and the second image is taken with a pixel size of 10 cm.
 In this paper, in order to detect of change of high-resolution images, first, the input images are registered in terms of spectral and spatial, and then feature images are extracted from each input image separately. In the next step, the differences images corresponding to high information content feature images are calculated. . The optimal difference images are mapped to the spherical space using selected statistical methods and in order to better analysis of the results. Otsu multi-thresholding method implemented on r component of sphere space.
 In the next step, the optimal difference image mapped to a spherical space is divided into non-overlapping blocks with the same dimensions; a cumulative hierarchical clustering method is applied for each block separately. In this case, the computational volume and space proposed in the hierarchical clustering method are reduced. The results of the cumulative clustering of the blocks are merged together and then the Markov random field method is used in order to regularize the results of the cluster in order to reduce noise.
In final clustering, the class values below the lowest Otsu threshold are known as unchanged pixels with high reliability and the values above the maximum threshold are determined as changed pixels. The class of middle interval is unknown. For determining, the class of middle interval the corresponded output of hierarchy clustering regularized with a random Markov field is used. In the last step, a vegetation and shadow mask is used for final post-processing.
Results and discussion
In order to an accurate assessment of the proposed method on the mentioned study area, a ground truth image with 11073 pixels has been used as a ground test image. The proposed method has shown an overall accuracy of 92.56 in the study area. The accuracy of detecting changed pixels shows 81.61% and the accuracy of detection unchanged pixels shows 92.77%. The false alarm percentage is 0.21 percent and the missed alarm accuracy is 0.0723 percent. For comparative evaluation, the proposed method is compared with the change vector analysis algorithm. In this section, the selected features in the feature extraction section are entered in the change analysis algorithm, and then the multi thresholding algorithm and shadow analysis used to create the final change map. This method has shown increasing the alarm in comparison with the proposed method. The accuracy of changed and un-changed pixels in the change vector analysis method is equal to 52.98 and 89.24%, respectively. Comparing these results with the results of the proposed method shows the efficiency of the proposed method.
In this paper, the new unsupervised change detection method presented based on the combination of multi thresholding and the hierarchical clustering algorithm. Compared to supervised methods that require training data, this method does not require training data. In this method, textural and spatial-spectral features are extracted from images with high spatial resolution, which covers the discussion of the importance of neighborhoods in images with high spatial resolution. In the next step, the extracted features that have a high information content are selected, which helps to reduce the redundancy of the information. The contrast images of the features with high information content are created to differentiate the location of the changes. Spherical computing space is considered as the basic computing space. In order to create a binary change map, two analyzes have been performed on the spherical computational space. First, the Otsu multi-thresholding method has been applied. The values of the smaller and larger thresholds have definite classes. But the value of the middle interval needs to be further analyzed using the hierarchical clustering method. In this section, the middle pixel class is examined, and then a final adjustment is performed using Markov field and shadow and vegetation analysis in order to post-process and prevent false changes. In this paper, the parameters of changed accuracy – unchanged accuracy - overall accuracy - false and missed alarms have been used to evaluate the accuracy of the proposed method with a ground accuracy map. In order to make a comparative study, the proposed method is compared with the change vector analysis method of the created feature space. The results show the efficiency of the proposed method.


1- Asokan, A. and J. Anitha (2019). “Change detection techniques for remote sensing applications: a survey.” Earth Science Informatics 12(2): 143-160.
2- Hao, M., W. Shi, Y. Ye, H. Zhang and K. Deng (2019). “A novel change detection approach for VHR remote sensing images by integrating multi-scale features.” International Journal of Remote Sensing 40(13): 4910-4933.
3- Hussain, M., D. Chen, A. Cheng, H. Wei and D. Stanley (2013). “Change detection from remotely sensed images: From pixel-based to object-based approaches.” ISPRS Journal of photogrammetry and remote sensing 80: 91-106.
4- Khanbani, S., A. Mohammadzadeh and M. Janalipour (2020). “A novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm.” Applied Geomatics: 1-17.
5- Khanbani, S., A. Mohammadzadeh and M. Janalipour (2020). “Unsupervised change detection of remotely sensed images from rural areas based on using the hybrid of improved Thresholding techniques and particle swarm optimization.” Earth Science Informatics: 1-14.
6- Lu, D., P. Mausel, E. Brondizio and E. Moran (2004). “Change detection techniques.” International journal of remote sensing 25(12): 2365-2401.
7- Lv, Z., T. Liu, C. Shi, J. A. Benediktsson and H. Du (2019). “Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images.” IEEE Access 7: 34425-34437.
8- Mitra, P., C. Murthy and S. K. Pal (2002). “Unsupervised feature selection using feature similarity.” IEEE transactions on pattern analysis and machine intelligence 24(3): 301-312.
9- Rensink, R. A. (2002). “Change detection.” Annual review of psychology 53(1): 245-277.
10- Rosin, P. L. (2002). “Thresholding for change detection.” Computer vision and image understanding 86
11- Saha, S., F. Bovolo and L. Bruzzone (2019). “Unsupervised deep change vector analysis for multiple-change detection in VHR images.” IEEE Transactions on Geoscience and Remote Sensing 57(6): 3677-3693.
12- Singh, A. (1989). “Review article digital change detection techniques using remotely-sensed data.” International journal of remote sensing 10(6): 989-1003.
13- Solano-Correa, Y. T., F. Bovolo and L. Bruzzone (2019). “An approach to multiple change detection in VHR optical images based on iterative clustering and adaptive thresholding.” IEEE Geoscience and Remote Sensing Letters 16(8): 1334-1338.
14- Solorio-Fernández, S., J. A. Carrasco-Ochoa and J. F. Martínez-Trinidad (2020). “A review of unsupervised feature selection methods.” Artificial Intelligence Review 53(2): 907-948.
15- Tan, K., Y. Zhang, X. Wang and Y. Chen (2019). “Object-based change detection using multiple classifiers and multi-scale uncertainty analysis.” Remote Sensing 11(3): 359.
16- Wang, X., P. Du, S. Liu, Y. Meng and C. Lin (2019). Unsupervised Change Detection in VHR Images Based on Morphological Profiles and Automated Training Sample Extraction. 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE.
17- Wei, C., P. Zhao, X. Li, Y. Wang and F. Liu (2019). “Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain.” International Journal of Remote Sensing 40(20): 7750-7766.
18- Wu, C., H. Chen, B. Do and L. Zhang (2019). “Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network.” arXiv preprint arXiv:1912.08628.
19- Zhan, T. and M. Gong (2019). A Hybrid Change Detection Method using Deep Feature Representations for VHR Images. 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE.
20- Zhou, S., Z. Xu and F. Liu (2016). “Method for determining the optimal number of clusters based on agglomerative hierarchical clustering.” IEEE Transactions on Neural Networks and learning systems 28(12): 3007-3017.