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	OPM2L: An optimal instance partition-based multi-metric learning method for heterogeneous dataset classification 期刊论文 OAI收割 INFORMATION SCIENCES, 2023, 卷号: 648, 页码: 18 作者: Deng, Huiyuan; Meng, Xiangzhu; Wang, Huibing; Feng, Lin \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2023/11/16 Multi-metric learning Alternating direction method Nearest-neighbor classification Riemannian manifold
	Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles 期刊论文 OAI收割 DRONES, 2023, 卷号: 7, 期号: 6, 页码: 353 作者: Shi, Weibo; Wang, Shaoqiang; Yue, Huanyin; Wang, Dongliang; Ye, Huping \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2023/08/22 fixed-wing UAVs multi-rotor UAVs semantic segmentation method tree species classification forest inventory
	基于多源遥感数据的塔里木河下游植被变化监测学位论文 OAI收割北京: 中国科学院大学, 2019 作者: 肖昊 \| 收藏 \| 浏览/下载：72/0 \| 提交时间：2021/12/10 塔里木河下游植被格局时空变化植被长势监测 SAVI 时序数据多方法分类 Lower Reaches of Tarim River Spatio-Temporal Change of Vegetation Pattern Monitoring of Vegetation Growth SAVI Time Series Data Multi-method Classification
	Improvement of urban land use and land cover classification approach in arid areas 会议论文 OAI收割 Proceedings of SPIE - The International Society for Optical Engineering, Image and Signal Processing for Remote Sensing XVI, Toulouse, France, 2010 Qian; Jing1; 2; Zhou; Qiming1; Chen; Xi2 收藏 \| 浏览/下载：38/0 \| 提交时间：2011/08/23 Arid regions - Image analysis - Image classification - Land use - Landforms - Maximum likelihood - Photography - Pixels - Remote sensing - Signal processing - Aerial Photographs - Arid area - Bare soils - Beijing-1 - Built-up areas - Classification approach - Classification results - Construction materials - Data sets - Discrete elements - Extraction accuracy - Field investigation - High resolution image - Land-cover types - Landsat ETM - Maximum likelihood classifications - Mixed pixel - Multi-sensor data - Object oriented - object-oriented classification method - Object-oriented processing - Spectral characteristics - Stony deserts - Urban areas - Urban change detection - Urban environments - Urban land use
	Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文 OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者: Wang D.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：27/0 \| 提交时间：2013/03/25 In feature-level fusion recognition system the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general there are two main missions. One is improving the recognition correct rate as soon as possible the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions this paper presents a more rational and accurate optimization Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.