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筛选

浏览/检索结果: 共180条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2024, 卷号: 17, 期号: 1, 页码: 29 作者:  Wang, Chunyang;  Yang, Kui;  Yang, Wei;  Li, Runkui;  Qiang, Haiyang   |  收藏  |  浏览/下载：17/0  |  提交时间：2024/02/19 Residual network  graph attention network  super-pixel segmentation  habitat quality  driving force analysis   Learning transferable cross-modality representations for few-shot hyperspectral and LiDAR collaborative classification 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 卷号: 126, 页码: 11 作者:  Dai, Mofan;  Xing, Shuai;  Xu, Qing;  Wang, Hanyun;  Li, Pengcheng   |  收藏  |  浏览/下载：9/0  |  提交时间：2024/02/26 Multimodal remote sensing data  Meta-learning  Few-shot learning  Cross-modality feature learning   Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection 期刊论文  OAI收割 REMOTE SENSING, 2023, 卷号: 15, 期号: 18 作者:  Wang, Nan;  Shi, Yuetian;  Li, Haiwei;  Zhang, Geng;  Li, Siyuan   |  收藏  |  浏览/下载：7/0  |  提交时间：2023/10/27 hyperspectral anomaly detection  deep learning  band selection  autoencoder   SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing 期刊论文  OAI收割 REMOTE SENSING, 2023, 卷号: 15, 期号: 8, 页码: 21 作者:  Wang, Jie;  Xu, Jindong;  Chong, Qianpeng;  Liu, Zhaowei;  Yan, Weiqing   |  收藏  |  浏览/下载：4/0  |  提交时间：2023/11/15 hyperspectral unmixing  spectral-spatial attention mechanism  deep learning  autoencoder   A Lake Extraction Method Combining the Object-Oriented Method with Boundary Recognition 期刊论文  OAI收割 LAND, 2023, 卷号: 12, 期号: 3, 页码: 545 作者:  Liu, Bingxue;  Wang, Wei;  Li, Wenping   |  收藏  |  浏览/下载：8/0  |  提交时间：2023/05/06 landsat  object-oriented  symmetrical boundary recognition  seed growth  watershed algorithm  lake extraction   Hyperspectral Image Super-Resolution Meets Deep Learning: A Survey and Perspective 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2023, 卷号: 10, 期号: 8, 页码: 1668-1691 作者:  Xinya Wang;  Qian Hu;  Yingsong Cheng;  Jiayi Ma   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/07/20 Deep learning  hyperspectral image  image fusion  image super-resolution  survey   Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images 期刊论文  OAI收割 IEEE Transactions on Geoscience and Remote Sensing, 2023, 卷号: 61 作者:  Qiu, Shi;  Ye, Huping;  Liao, Xiaohan   |  收藏  |  浏览/下载：9/0  |  提交时间：2023/11/10 3-D convolutional neural network (CNN)  depth characteristic  hyperspectrum  multilevel  remote sensing  squeeze and excitation network (SENet)   Hyperspectral Band Selection With Iterative Graph Autoencoder 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 13 作者:  Zhou, Yuan;  Yao, Qingren;  Huo, Shuwei;  Li, Xiaofeng   |  收藏  |  浏览/下载：4/0  |  提交时间：2023/12/07 Feature extraction  Iterative methods  Symmetric matrices  Hyperspectral imaging  Convolution  Correlation  Task analysis  Graph autoencoder (GAE)  graph representation  hyperspectral band selection (BS)  representativeness   Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images 期刊论文  OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 15 作者:  Qiu, Shi;  Ye, Huping;  Liao, Xiaohan   |  收藏  |  浏览/下载：6/0  |  提交时间：2023/11/29 Feature extraction  Hyperspectral imaging  Sea measurements  Convolutional neural networks  Three-dimensional displays  Remote sensing  Water quality  3-D convolutional neural network (CNN)  depth characteristic  hyperspectrum  multilevel  remote sensing  squeeze and excitation network (SENet)   Segmentation of Thyroid Nodules Based on Hyperspectral Images 会议论文  OAI收割 Chongqing, China, 2023-12-08 作者:  Wang, Junjie;  Tao, Chenglong;  Du, Jian;  Hu, Bingliang;  Zhang, Zhoufeng   |  收藏  |  浏览/下载：21/0  |  提交时间：2024/04/10 Hyperspectral images  thyroid nodules  segmentation