1 |
Achanta, R. and S. Susstrunk, 2017. Superpixels and Polygons using Simple Non-Iterative Clustering, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, Jul. 21-26, pp. 4895-4904.
|
2 |
Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, 2012. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2274-2282.
DOI
|
3 |
Byun, Y., Y. Han, and T. Chae, 2015. Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images, Remote Sensing, 7(8): 10347-10363.
DOI
|
4 |
Chen, G., K. Zhao, and R. Powers, 2014. Assessment of the image misregistration effects on objectbased change detection, ISPRS Journal of Photogrammetry and Remote Sensing, 87: 19-27.
DOI
|
5 |
Choi, J., G. Kim, N. Park, H. Park, and S. Choi, 2017. A Hybrid Pansharpening Algorithm of VHR Satellite Images that Employs Injection Gains Based on NDVI to Reduce Computational Costs, Remote Sensing, 9(10): 976.
DOI
|
6 |
Chung, M., Y. Han, J. Choi, and Y. Kim, 2018. Evaluation of Object-based Change Detection using SLIC Superpixels, Proc. of Korean Society of Remote Sensing Fall Conference, Muju, Korea, Oct. 24-26, pp. 19-22 (in Korean).
|
7 |
Ester, M., H. P. Kriegel, J. Sander, and X. Xu, 1996. A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, Aug. 2-4, vol. 96, no. 34, pp. 226-231.
|
8 |
Han, Y., T. Kim, and C. Lee, 2018. Comparison of Segmentation Inputs for Object-based Unsupervised Change Detection between Very-high-resolution Bi-temporal Images, Proc. of Asian Conference on Remote Sensing, vol. 2, pp. 1165-1169.
|
9 |
Hou, B., Y. Wang, and Q. Liu, 2016. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images, Sensors, 16(9): 1377.
DOI
|
10 |
Kavzoglu, T. and H. Tonbu, 2017. Selecting Optimal SLIC Superpixels Parameters by Using Discrepancy Measures, Proc. of Asian Conference on Remote Sensing, New Delhi, India, Oct. 23-27, pp. 1-7.
|
11 |
Powers, D. M., 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, Journal of Machine Learning Technologies, 2(1): 37-63.
|
12 |
Kovesi, P. D., 2000. MATLAB and Octave Functions for Computer Vision and Image Processing, http://www.peterkovesi.com/matlabfns, Accessed on Dec. 17, 2018.
|
13 |
Li, Y., Y. Tan, J. Deng, Q. Wen, and J. Tian, 2015. Cauchy Graph Embedding Optimization for Built-Up Areas Detection from High-Resolution Remote Sensing Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 2078-2096.
DOI
|
14 |
Neubert, P. and P. Protzel, 2012. Superpixel benchmark and comparison, Forum Bildverarbeitung, 6: 205-208.
|
15 |
Ru, H., X. Yang, D. Peng, and P. Huang, 2014. Superparsing based change detection in high resolution remote sensing imagery, Proc. of 2014 12th International Conference on Signal Processing, Hangzhou, China, Oct. 19-23, pp. 996-999.
|
16 |
Vargas, J. E., P. T. Saito, A. X. Falcao, P. J. Rezende, and J. A. Santos, 2014. Superpixel-Based Interactive Classification of Very High Resolution Images, Proc. of 2014 27th SIBGRAPI Conference on Graphics, Patterns and Images, Rio de Janeiro, Brazil, Aug. 27-30, pp. 173-179.
|
17 |
Wang, B., J. Choi, S. Choi, S. Lee, P. Wu, and Y. Gao, 2017. Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images, Remote Sensing, 9(8): 804.
DOI
|
18 |
Wu, Z., Z. Hu, and Q. Fan, 2012. Superpixel-based unsupervised change detection using multidimensional change vector analysis and SVMbased classification, Proc. of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, Aug. 25-Sep. 1, vol. I-7, pp. 257-262.
|