1 |
Choi, J. (2015), Unsupervised change detection for very high-spatial resolution satellite imagery by using object-based IR-MAD algorithm, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 4, pp. 297-304. (in Korean with English abstract)
DOI
|
2 |
Choi, J., Kim, G., Park, N., Park, H., and Choi, S. (2017), A hybrid pansharpening algorithm of VHR satellite images that employs injection gains based on NDVI to reduce computational costs, Remote Sensing, Vol. 9, No. 10. pp. 976.
DOI
|
3 |
Chung, M., Han, Y., Choi, J., and Kim, Y. (2018), Optimal parameter analysis and evaluation of change detection for SLIC-based superpixel techniques using KOMPSAT Data, Korean Journal of Remote Sensing, Vol. 34, No. 6_3, pp. 1427-1443. (in Korean with English abstract)
DOI
|
4 |
Dellinger, F., Delon, J., Gousseau, Y., Michel, J., and Tupin, F. (2014), Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach, In 2014 IEEE Geoscience and Remote Sensing Symposium 2014, 13-18 July, Quebec, Canada, pp. 1281-1284.
|
5 |
Jeong, J. (2005), Developments of urban change detection methods according to spatial resolution of satellite images -application of KOMPSAT 1 images into urban area-, The Geographical Journal of Korea, Vol. 39, No. 1, pp. 161-170. (in Korean with English abstract)
|
6 |
Deng, J.S., Wang, K., Deng, Y.H., and Qi, G.J. (2008), PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data, International Journal of Remote Sensing, Vol. 29, No. 16, pp. 4823-4838.
DOI
|
7 |
Han, Y., Kim, T., Han, S., and Song, J. (2017), Change detection of urban development over large area using KOMPSAT optical imagery, Korean Journal of Remote Sensing, Vol. 33, No. 6-3, pp. 1223-1232. (in Korean with English abstract)
DOI
|
8 |
Hussain, M., Chen, D., Cheng, A., Wei, H., and Stanley, D. (2013), Change detection from remotely sensed images: from pixel-based to object-based approaches, ISPRS Journal of photogrammetry and remote sensing, Vol. 80, pp. 91-106.
DOI
|
9 |
Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018), 3D convolutional neural networks for crop classification with multi-temporal remote sensing images, Remote Sensing, Vol. 10, No. 1, pp. 75.
DOI
|
10 |
Lim, K., Jin, D., and Kim, C.S. (2018), Change detection in high resolution satellite images using an ensemble of convolutional neural networks, In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 12-15 November, Honolulu, Hawaii, USA, pp. 509-515.
|
11 |
Li, Y., Zhang, H., Xue, X., Jiang, Y., and Shen, Q. (2018), Deep learning for remote sensing image classification: A survey, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8, No. 6, pp. e1264.
|
12 |
Liu, B., Yu, X., Yu, A., and Wan, G. (2018), Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification, Journal of Applied Remote Sensing, Vol. 12, No. 2, pp. 026028.
|
13 |
Song, A., Choi, J., Han, Y., and Kim, Y. (2018), Change detection in hyperspectral images using recurrent 3D fully convolutional networks, Remote Sensing, Vol. 10, No. 11, pp. 1827.
DOI
|
14 |
Lyu, H., Lu, H., and Mou, L. (2016), Learning a transferable change rule from a recurrent neural network for land cover change detection, Remote Sensing, Vol. 8, No. 6, pp. 506.
DOI
|
15 |
Mei, S., Yuan, X., Ji, J., Zhang, Y., Wan, S., and Du, Q. (2017), Hyperspectral image spatial super-resolution via 3D full convolutional neural network, Remote Sensing, Vol. 9, No. 11, pp. 1139.
DOI
|
16 |
Mou, L., Bruzzone, L., and Zhu, X.X. (2019), Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 2, pp. 924-935.
DOI
|
17 |
Song, A. (2019), A Novel Deep Learning Framework for Multi-Class Change Detection of Hyperspectral Images, Doctoral Thesis, Seoul National University, Seoul, Korea, 137p.
|
18 |
Tan, K., Zhang, Y., Wang, X., and Chen, Y. (2019), Object-based change detection using multiple classifiers and multi-scale uncertainty analysis, Remote Sensing, Vol. 11, No. 3, pp. 359.
DOI
|
19 |
Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018), A fast dense spectral-spatial convolution network framework for hyperspectral images classification, Remote Sensing, Vol. 10, No. 7, pp. 1068.
DOI
|
20 |
Wang, Q., Yuan, Z., Du, Q., and Li, X. (2018), Getnet: A general end-to-end 2-d CNN framework for hyperspectral image change detection, IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 1, pp. 3-13.
DOI
|
21 |
Wang, Q., Zhang, X., Chen, G., Dai, F., Gong, Y., and Zhu, K. (2018), Change detection based on Faster R-CNN for high-resolution remote sensing images, Remote sensing letters, Vol. 9, No. 10, pp. 923-932.
DOI
|
22 |
Zhang, C., Wei, S., Ji, S., and Lu, M. (2019), Detecting large-scale urban land cover changes from very high resolution remote sensing images using CNN-based classification, ISPRS International Journal of Geo-Information, Vol. 8, No. 4, pp. 189.
DOI
|
23 |
Wiratama, W., Lee, J., Park, S. E., and Sim, D. (2018), Dual-dense convolution network for change detection of high-resolution panchromatic imagery, Applied Sciences, Vol. 8, No. 10, pp. 1785.
DOI
|
24 |
Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015), Convolutional LSTM network: A machine learning approach for precipitation nowcasting, In Advances in Neural Information Processing Systems, 7-12 December, Montreal, Canada, pp. 802-810.
|
25 |
Yu, H., Yang, W., Hua, G., Ru, H., and Huang, P. (2017), Change detection using high resolution remote sensing images based on active learning and markov random fields, Remote Sensing, Vol. 9, No. 12, pp. 1233.
DOI
|