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Object-Based Tree Species Classification Using Airborne Hyperspectral Images and LiDAR Data

Wu, Zhang
2019 Forests  
This study combined airborne hyperspectral data with simultaneously acquired LiDAR data, to evaluate the capability of feature combinations and k-nearest neighbor (KNN) and support vector machine (SVM)  ...  In general, the combination of different features can improve the classification accuracy, and the proposed strategies and methods are effective for the identification of tree species at complex forest  ...  Bin Zhang for their help in the fieldwork.  ... 
doi:10.3390/f11010032 fatcat:azsawd4vnbashdd7p3nernzoje

Table of Contents

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Bhatt 2148 Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data . . . . . . . . . . . . . . . . . . . . . . . . . . W.  ...  Skakun 2260 On Bird Species Diversity and Remote Sensing-Utilizing Lidar and Hyperspectral Data to Assess the Role of Vegetation Structure and Foliage Characteristics as Drivers of Avian Diversity . .  ... 
doi:10.1109/jstars.2019.2926601 fatcat:xr4yhjfp6vcynazazvsuye7x2q

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Lopez, J.F  ...  ., +, JSTARS March 2019 910-919 Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data.  ...  ., +, JSTARS Nov. 2019 4275-4283 Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4399-4409 A Multiscale Random Forest Kernel for Land Cover Classification.  ...  ., +, JSTARS 2020 1206-1217 Pattern classification A Multiscale Random Forest Kernel for Land Cover Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm

Jingjing Cao, Kai Liu, Li Zhuo, Lin Liu, Yuanhui Zhu, Liheng Peng
2021 International Journal of Applied Earth Observation and Geoinformation  
detection and ranging (LiDAR) data, and the rotation forest (RoF) ensemble learning algorithm.  ...  Results showed that the combined hyperspectral and LiDAR data produced satisfactory results for all three classifiers with overall accuracy (OA) higher than 95%, and the proposed method achieved the highest  ...  We also acknowledge the support of NBL Imaging System Ltd. for providing the UAV-based Nano-hyperspec hyperspectral data, and Guangzhou Jiantong Surveying and Mapping Geoinformation Technology Co., Ltd  ... 
doi:10.1016/j.jag.2021.102414 fatcat:kox7acm65ndoneou6bclc33eim

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

Xin Zhang, Liangxiu Han, Yingying Dong, Yue Shi, Wenjiang Huang, Lianghao Han, Pablo González-Moreno, Huiqin Ma, Huichun Ye, Tam Sobeih
2019 Remote Sensing  
Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection.  ...  Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used.  ...  Acknowledgments: We thank the anonymous reviewers for reviewing the manuscript and providing comments to improve the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11131554 fatcat:xqhb3md5sfeifczd7can3fkhxu

Table of contents

2021 IEEE Geoscience and Remote Sensing Letters  
Zhang, and L. Li 1655 Lidar Data The Development and Evaluation of a Backpack LiDAR System for Accurate and Efficient Forest Inventory ........ .............................................. Y.  ...  Ben Hamida 1620 Hyperspectral Data An Iterative Random Training Sample Selection Approach to Constrained Energy Minimization for Hyperspectral Image Classification .....................................  ... 
doi:10.1109/lgrs.2021.3105736 fatcat:gb4k7oxfkzb7lflayjkcf4uvtm

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Foerster, S  ...  ., +, JSTARS Sept. 2014 3945-3955 A New Hybrid Strategy Combining Semisupervised Classification and Unmixing of Hyperspectral Data.  ...  ., +, JSTARS June 2014 2697-2703 A New Hybrid Strategy Combining Semisupervised Classification and Un- mixing of Hyperspectral Data.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval

Devulapalli Sudheer, Rajakumar Krishnan
2019 Advances in Science, Technology and Engineering Systems  
Content Based Image Retrieval (CBIR) for remote sensing image data is a tedious process due to high resolution and complexity of image interpretation.  ...  GLCM texture features are extracted from the Gabor magnitude response. The novel feature set by combining the CCV and GLCM using curvelet and Gabor filter is developed.  ...  from the Hyperspectral data.  ... 
doi:10.25046/aj040634 fatcat:elllei6nfvaurdxkvy2uiy36pe

2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53

2015 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS Nov. 2015 6223-6240 Spectral-Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields.  ...  ., +, TGRS Jan. 2015 405-415 Spectral-Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

An Improved Feature Set for Hyperspectral Image Classification: Harmonic Analysis Optimized by Multi-scale Guided Filter (HGFM)

Wei Zhang, Peijun Du, Cong Lin, Pingjie Fu, Xin Wang, Xuyu Bai, Hongrui Zheng, Junshi Xia, Alim Samat
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Three commonly used HSIs are used for experiments, and different feature sets are evaluated by comparing EL and rotation forest, support vector machine optimized by particle swarm optimization, random  ...  forest, and others.  ...  The suitable algorithms, including random forest (RF), rotation forest (RoF), support vector machine optimized by particle swarm optimization (PSO-SVM), K-nearest neighbor (KNN), and extreme learning  ... 
doi:10.1109/jstars.2020.3006772 fatcat:majgqjrqlvhnbnh2zoi5mcxi7u

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674  ...  and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  ., +, TGRS Aug. 2019 5701-5715 A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Wang 351 Inverse Coefficient of Variation Feature and Multilevel Fusion Technique for Hyperspectral and LiDAR Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Ghamisi 1937 A Multiscale Random Forest Kernel for Land Cover Classification .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Li 6260 Special Section: Sparse and Collaborative Representation for Remote Sensing Data Processing Neighborhood Activity-Driven Representation for Hyperspectral Imagery Classification . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
For more information please see "Combination of Satellite Observations and Machine Learning Method for Internal Wave Forecast in the Sulu and Celebes Seas," by Zhang and Li, which begins on page 2822.  ...  Li 3383 Multiscale Residual Network With Mixed Depthwise Convolution for Hyperspectral Image Classification ............ ................................................................................  ... 
doi:10.1109/tgrs.2021.3063896 fatcat:miqrb4or7jbujhr2aqlvhxq3oi

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Eryang Chen, Ruichun Chang, Kaibo Shi, Ansheng Ye, Fang Miao, Jianghong Yuan, Ke Guo, Youhua Wei, Yiping Li, Zi-Peng Wang
2021 Discrete Dynamics in Nature and Society  
Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification.  ...  More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.  ...  Table 1 : 1 A comparison of different methods for hyperspectral image classification.  ... 
doi:10.1155/2021/9998185 fatcat:bwrxa3xnizba5g2sy7q6kv24py
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