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| 1 | Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe(Yellow) River Estuary wetland显示文摘This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe(Yellow) River Estuary coastal wetland.The results show that:(1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%.(2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%.(3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80.(4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72%respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods. | Yabin Hu Jie Zhang Yi Ma Xiaomin Li Qinpei Sun Jubai An | 2019 | Acta Oceanologica Sinica2019，38，5： | 5 |
| 2 | Combining fuzzy theory and a genetic algorithm for satellite image edge detection显示文摘 | JUBAI A JING B YANG J | 2006 | International Journal of Remote Sensing2006，27，14： | 1 |
| 3 | A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images显示文摘 | Liao Shenlong An Jubai | 2015 | IEEE Geoscience and Remote Sensing Letters2015，12，5： | 1 |
| 4 | A novel edge detection algorithm based on global minimization active contour model for oil slick infrared aerial image显示文摘 | Jing Yu An Jubai Liu Zhaoxia | 2011 | IEEE Transactions on Geoscience and Remote Sensing2011，496，： | 1 |
| 5 | A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration显示文摘 | Liu Zhaoxia An Jubai Jing Yu | 2011 | IEEE Transactions on Geoscience and Remote Sensing2011，4，： | 1 |
| 6 | A two-stage registration algorithm for oil spill aerial image by invariants-based similarity and improved ICP显示文摘 | Liu Zhaoxia An Jubai Li Li | 2011 | International Journal of Remote Sensing2011，3213，： | 1 |
| 7 | A novel aromatically functional polymeric ionic liquid as sorbent material for solid-phase microextraction显示文摘 | Juanjuan Feng Min Sun Jubai Li Xia Liu Shengxiang Jiang | 2012 | Journal of Chromatography A2012，，： | 1 |
| 8 | A simple and robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration显示文摘 | Liu Zhaoxia An Jubai Jing Yu | 2012 | IEEE Transactions on Geoscience and Remote Sensing2012，50，2： | 1 |
| 9 | A simple robust feature point matching algorithm based on restricted spatial order constraints for aerial image registration 显示文摘 | LIU Zhaoxia AN Jubai JING Yu | 2011 | IEEE Transactions on Geosciences and Remote Sensing2011，8，4： | 1 |
| 10 | A Simple and RobustFeature Point Matching Algorithm Based on RestrictedSpatial Order Constraints for Aerial Image Registration显示文摘 | Liu Zhaoxia An Jubai Jing Yu | 2012 | IEEE Geosciences and Remote Sensing Society2012，50，2： | 1 |
| 11 | Coastal wetland hyperspectral classification under the collaborative of subspace partition and infinite probabilistic latent graph ranking显示文摘The abundance of spectral information provided by hyperspectral imagery offers great benefits for many applications.However,processing such high-dimensional data volumes is a challenge because there may be redundant bands owing to the high interband correlation.This study aimed to reduce the possibility of“dimension disaster”in the classification of coastal wetlands using hyperspectral images with limited training samples.The study developed a hyperspectral classification algorithm for coastal wetlands using a combination of subspace partitioning and infinite probabilistic latent graph ranking in a random patch network(the SSP-IPLGR-RPnet model).The SSP-IPLGR-RPnet approach applied SSP techniques and an IPLGR algorithm to reduce the dimensions of hyperspectral data.The RPnet model overcame the problem of dimension disaster caused by the mismatch between the dimensionality of hyperspectral bands and the small number of training samples.The results showed that the proposed algorithm had a better classification performance and was more robust with limited training data compared with that of several other state-of-the-art methods.The overall accuracy was nearly 4%higher on average compared with that of multi-kernel SVM and RF algorithms.Compared with the EMAP algorithm,MSTV algorithm,ERF algorithm,ERW algorithm,RMKL algorithm and 3D-CNN algorithm,the SSP-IPLGR-RPnet algorithm provided a better classification performance in a shorter time. | HU YaBin REN GuangBo MA Yi YANG JunFang WANG JianBu AN JuBai LIANG Jian MA YuanQing SONG XiuKai | 2022 | Science China(Technological Sciences)2022，65，4： | 0 |
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