2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)最新文献_第7页

Interannual variation of vegetation greenness and water body surface area and their relationship with settlement development in Xinjiang, China 新疆植被绿度和水体面积的年际变化及其与聚落发展的关系

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730903

Qingting Li

引用次数: 0

Gabor-based active learning for hyperspectral image classification 基于gabor主动学习的高光谱图像分类

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729634

Jie Hu, Chenying Liu, Lin He, Jun Li

{"title":"Gabor-based active learning for hyperspectral image classification","authors":"Jie Hu, Chenying Liu, Lin He, Jun Li","doi":"10.1109/IGARSS.2016.7729634","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729634","url":null,"abstract":"Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data are often highly mixed, i.e., most samples fluctuate in a local density areas. In this case, the potential of active learning for effective training sample selection is more limited. In order to address this relevant issue, we develop a new Gabor-based active learning approach for hyperspectral image classification, which consists of two main steps. First, we use a Gabor filter for feature extraction, which aims at bringing the data into a discriminative space. Then, we perform active learning to find the most informative training samples in the low density areas prior to the final classification. Our experimental results, conducted using two real hyperspectral datasets, indicate that the proposed Gabor-based approach can greatly improve the potential of active learning for classification purposes.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116817913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forest vertical structure parameters extraction from airborne X-band InSAR data 基于机载x波段InSAR数据的森林垂直结构参数提取

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729031

Q. Feng, E. Chen, Zeng-yuan Li, Lan Li, Lei Zhao

引用次数: 1

Improving the reference network in wide-area Persistent Scatterer Interferometry for non-urban areas 非城市地区广域持久散射干涉测量参考网络的改进

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729370

Kanika Goel, N. Adam, R. Shau, Fernando Rodríguez González

引用次数: 9

Spectrum analysis of SAR image in polar grid system for back projection algorithm 极坐标网格系统SAR图像光谱分析的反投影算法

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730318

Song Zhou, Lei Yang, Lifan Zhao, G. Bi

引用次数: 1

A display method allocating SAR image and ATI phase map to value and saturation, respectively 一种将SAR图像和ATI相位图分别分配到值和饱和度的显示方法

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730704

Tomoya Yamaoka, T. Hara

引用次数: 0

SAR scattering and imaging with focusing by an extended target modeL 基于扩展目标模型的SAR散射与聚焦成像

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729276

Chiung-Shen Ku, Kun Shan Chen, S. Tjuatja, P. Chang, Yang-Lang Chang

{"title":"SAR scattering and imaging with focusing by an extended target modeL","authors":"Chiung-Shen Ku, Kun Shan Chen, S. Tjuatja, P. Chang, Yang-Lang Chang","doi":"10.1109/IGARSS.2016.7729276","DOIUrl":"https://doi.org/10.1109/IGARSS.2016.7729276","url":null,"abstract":"SAR is a complex system that integrates two major parts: data collector and image formatter [1-2]. In the phase of data collection, radar transmits electromagnetic waves toward the target and receives the scattered waves. The transmitted signal can be modulated into certain types, commonly linearly frequency modulated with pulse or continuous waveform. The process involves signal transmission from generator, through various types of guided device, to antenna, by which the signal is radiated into free space, and then undergoes propagation. The measured scattered signal been made in bistatic or monostatic configurations is essentially in time-frequency (delay time - Doppler frequency) domain. The role of image formatter is then to map the time-frequency data into spatial domain where the targets are located. The mapping from the data domain to image domain, and eventually, into target or object domain must minimize both geometric and radiometric distortions. Essentially, two models that define the SAR operational process: physical model and system model. This paper concentrates on the physical process of a SAR system from wave scattering to imaging. System simulation based on the stationary (frequency modulation continuous wave) FMCW is developed and implemented for both point target and extended target. To further validate the simulation and thus our physical understanding of the imaging chain, measurements at aniconic chamber with two mental spheres and two dielectric spheres displaced with varying spacing were conducted. Good agreement between the simulated by extend target model and real measured SAR images is obtained.","PeriodicalId":179622,"journal":{"name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129557787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alteration anomaly information extraction using hyperspectral remote sensing in coalbed methane enrichment 煤层气富集高光谱遥感蚀变异常信息提取

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730405

Guangwei Zhen, Li Chen, Chao Chen, Biyun Guo

引用次数: 5

Parallel processing for accelerated Mean Shift algorithm based on TBB 基于TBB的加速Mean Shift算法的并行处理

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7730659

Ling Ding, Hongyi Li

引用次数: 1

Validation of the new SRTM digital elevation model (NASADEM) with ICESAT/GLAS over the United States 利用ICESAT/GLAS在美国上空验证新的SRTM数字高程模型(NASADEM)

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2016-07-10 DOI: 10.1109/IGARSS.2016.7729835

M. Simard, M. Neumann, S. Buckley

引用次数: 13