Change Detection and Image Time-Series Analysis 1

Здесь есть возможность читать онлайн «Change Detection and Image Time-Series Analysis 1» — ознакомительный отрывок электронной книги совершенно бесплатно, а после прочтения отрывка купить полную версию. В некоторых случаях можно слушать аудио, скачать через торрент в формате fb2 и присутствует краткое содержание. Жанр: unrecognised, на английском языке. Описание произведения, (предисловие) а так же отзывы посетителей доступны на портале библиотеки ЛибКат.

Change Detection and Image Time-Series Analysis 1: краткое содержание, описание и аннотация

Предлагаем к чтению аннотацию, описание, краткое содержание или предисловие (зависит от того, что написал сам автор книги «Change Detection and Image Time-Series Analysis 1»). Если вы не нашли необходимую информацию о книге — напишите в комментариях, мы постараемся отыскать её.

Change Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities. <br /><br />Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.

Change Detection and Image Time-Series Analysis 1 — читать онлайн ознакомительный отрывок

Ниже представлен текст книги, разбитый по страницам. Система сохранения места последней прочитанной страницы, позволяет с удобством читать онлайн бесплатно книгу «Change Detection and Image Time-Series Analysis 1», без необходимости каждый раз заново искать на чём Вы остановились. Поставьте закладку, и сможете в любой момент перейти на страницу, на которой закончили чтение.

Тёмная тема
Сбросить

Интервал:

Закладка:

Сделать

1.7. Acknowledgements

This work was supported by the Natural Science Foundation of China under Grant 42071324, 41601354, and by the Shanghai Rising-Star Program (21QA1409100).

1.8. References

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S. (2012). Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence , 34(11), 2274–2282.

Ban, Y. and Yousif, O. (2016). Change Detection Techniques: A Review . Springer International Publishing, Cham.

Bazi, Y., Bruzzone, L., Melgani, F. (2005). An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing , 43(4), 874–887.

Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R. (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing , 43(3), 480–491.

Bouziani, M., Goïta, K., He, D.-C. (2010). Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS Journal of Photogrammetry and Remote Sensing , 65(1), 143–153 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S092427160900121X.

Bovolo, F. (2009). A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geoscience and Remote Sensing Letters , 6(1), 33–37.

Bovolo, F. and Bruzzone, L. (2007a). A split-based approach to unsupervised change detection in large-size multitemporal images: Application to tsunami-damage assessment. IEEE Transactions on Geoscience and Remote Sensing , 45(6), 1658–1670.

Bovolo, F. and Bruzzone, L. (2007b). A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Transactions on Geoscience and Remote Sensing , 45(1), 218–236.

Bovolo, F. and Bruzzone, L. (2011). An adaptive thresholding approach to multiple-change detection in multispectral images. IEEE International Geoscience and Remote Sensing Symposium , 233–236.

Bovolo, F. and Bruzzone, L. (2015). The time variable in data fusion: A change detection perspective. IEEE Geoscience and Remote Sensing Magazine , 3(3), 8–26.

Bovolo, F., Marchesi, S., Bruzzone, L. (2012). A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing , 50(6), 2196–2212.

Bruzzone, L. and Bovolo, F. (2013). A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proceedings of the IEEE , 101(3), 609–630.

Bruzzone, L. and Prieto, D.F. (2000a). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing , 38(3), 1171–1182.

Bruzzone, L. and Prieto, D.F. (2000b). A minimum-cost thresholding technique for unsupervised change detection. International Journal of Remote Sensing , 21(18), 3539–3544 [Online]. Available at: https://doi.org/10.1080/014311600750037552.

Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k -means clustering. IEEE Geoscience and Remote Sensing Letters , 6(4), 772–776.

Celik, T. and Ma, K.K. (2011). Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Transactions on Geoscience and Remote Sensing , 49(2), 706–716.

Chen, J., Gong, P., He, C., Pu, R., Shi, P. (2003). Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering and Remote Sensing , 69(4), 369–379.

Chen, G., Hay, G.J., Carvalho, L.M.T., Wulder, M.A. (2012). Object-based change detection. International Journal of Remote Sensing , 33(14), 4434–4457 [Online]. Available at: https://doi.org/10.1080/01431161.2011.648285.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E. (2004). Review article digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing , 25(9), 1565–1596 [Online]. Available at: https://doi.org/10.1080/0143116031000101675.

Dalla Mura, M., Benediktsson, J.A., Waske, B., Bruzzone, L. (2010). Morphological attribute profiles for the analysis of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing , 48(10), 3747–3762.

Du, P., Liu, S., Gamba, P., Tan, K., Xia, J. (2012). Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 5(4), 1076–1086.

Du, P., Liu, S., Xia, J., Zhao, Y. (2013). Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion , 14(1), 19–27 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S1566253512000565.

Falco, N., Mura, M.D., Bovolo, F., Benediktsson, J.A., Bruzzone, L. (2013). Change detection in VHR images based on morphological attribute profiles. IEEE Geoscience and Remote Sensing Letters , 10(3), 636–640.

Ghosh, A., Mishra, N.S., Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences , 181(4), 699–715 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0020025510005153.

Han, P., Gong, J., Li, Z. (2008). A new approach for choice of optimal spatial scale in image classification based on entropy. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 033(7), 676–679.

Han, Y., Javed, A., Jung, S., Liu, S. (2020). Object-based change detection of very high resolution images by fusing pixel-based change detection results using weighted Dempster–Shafer theory. Remote Sensing , 12(6) [Online]. Available at: https://www.mdpi.com/2072-4292/12/6/983.

Huang, X., Zhang, L., Zhu, T. (2014). Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 7(1), 105–115.

Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing , 80, 91–106 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0924271613000804.

Kaszta, A., Van De Kerchove, R., Ramoelo, A., Cho, M.A., Madonsela, S., Mathieu, R., Wolff, E. (2016). Seasonal separation of African savanna components using WorldView-2 imagery: A comparison of pixel- and object-based approaches and selected classification algorithms. Remote Sensing , 8(9) [Online]. Available at: https://www.mdpi.com/2072-4292/8/9/763.

Keshava, N. (2004). Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Transactions on Geoscience and Remote Sensing , 42(7), 1552–1565.

Khan, S.H., He, X., Porikli, F., Bennamoun, M. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing , 55(9), 5407–5423.

Leichtle, T., Geiß, C., Wurm, M., Lakes, T., Taubenböck, H. (2017). Unsupervised change detection in VHR remote sensing imagery – An object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation , 54, 15–27 [Online]. Available at: http://www.sciencedirect.com/science/article/pii/S0303243416301490.

Читать дальше
Тёмная тема
Сбросить

Интервал:

Закладка:

Сделать

Похожие книги на «Change Detection and Image Time-Series Analysis 1»

Представляем Вашему вниманию похожие книги на «Change Detection and Image Time-Series Analysis 1» списком для выбора. Мы отобрали схожую по названию и смыслу литературу в надежде предоставить читателям больше вариантов отыскать новые, интересные, ещё непрочитанные произведения.


Отзывы о книге «Change Detection and Image Time-Series Analysis 1»

Обсуждение, отзывы о книге «Change Detection and Image Time-Series Analysis 1» и просто собственные мнения читателей. Оставьте ваши комментарии, напишите, что Вы думаете о произведении, его смысле или главных героях. Укажите что конкретно понравилось, а что нет, и почему Вы так считаете.

x