Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines

Very High ◽

Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines

Sensor Letters ◽

10.1166/sl.2011.1361 ◽

2011 ◽

Vol 9 (3) ◽

pp. 997-1005 ◽

Cited By ~ 6

Author(s):

Jing Shen ◽

Jiping Liu ◽

Xiangguo Lin ◽

Rong Zhao ◽

Shenghua Xu

Keyword(s):

Support Vector Machines ◽

Spatial Resolution ◽

Satellite Imagery ◽

High Spatial Resolution ◽

Mean Shift ◽

Support Vector ◽

Object Based ◽

Vector Machines ◽

Very High

Classification of Very High Spatial Resolution Imagery Based on the Fusion of Edge and Multispectral Information

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.74.12.1585 ◽

2008 ◽

Vol 74 (12) ◽

pp. 1585-1596 ◽

Cited By ~ 10

Author(s):

Xin Huang ◽

Liangpei Zhang ◽

Pingxiang Li

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Very High ◽

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 ◽

A multilevel hierarchical approach to classification of high spatial resolution images with support vector machines

10.1109/igarss.2004.1369083 ◽

2004 ◽

Cited By ~ 1

Author(s):

Lorenzo Carlin Lorenzo Bruzzone

Keyword(s):

Support Vector Machines ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Support Vector ◽

Hierarchical Approach ◽

Vector Machines

Potential of Multi-scale Completed Local Binary Pattern for Object Based Classification of Very High Spatial Resolution Imagery

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-021-01311-y ◽

2021 ◽

Author(s):

Radhia Chairet ◽

Yassine Ben Salem ◽

Mohamed Aoun

Keyword(s):

Spatial Resolution ◽

Local Binary Pattern ◽

High Spatial Resolution ◽

Multi Scale ◽

Object Based ◽

Very High ◽

Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2013.2282469 ◽

2014 ◽

Vol 11 (5) ◽

pp. 940-944 ◽

Cited By ~ 7

Author(s):

Hua Zhang ◽

Wenzhong Shi ◽

Yunjia Wang ◽

Ming Hao ◽

Zelang Miao

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Shape Feature ◽

Very High ◽

An automated flood detection framework for very high spatial resolution imagery

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6947607 ◽

2014 ◽

Cited By ~ 7

Author(s):

Andrea Scarsi ◽

William J. Emery ◽

Gabriele Moser ◽

Fabio Pacifici ◽

Sebastiano B. Serpico

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Flood Detection ◽

Very High ◽

Hierarchical Classification of Brazilian Savanna Physiognomies Using Very High Spatial Resolution Image, Superpixel and Geobia

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898649 ◽

2019 ◽

Author(s):

Alana Kasahara Neves ◽

Thales Sehn Korting ◽

Cesare Di Girolamo Neto ◽

Anderson Reis Soares ◽

Leila Maria Garcia Fonseca

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Hierarchical Classification ◽

Brazilian Savanna ◽

High Spatial Resolution Image ◽

Resolution Image ◽

Very High

Long Term Soil Productivity study using very high spatial resolution imagery

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6721202 ◽

2013 ◽

Cited By ~ 2

Author(s):

Kongwen Zhang ◽

Mike Curran ◽

Justin Robinson ◽

Baoxin Hu

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Soil Productivity ◽

Very High ◽

Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.014521 ◽

2019 ◽

Vol 13 (01) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Saiping Xu ◽

Qianjun Zhao ◽

Kai Yin ◽

Feifei Zhang ◽

Dingbang Liu ◽

...

Keyword(s):

Support Vector Machines ◽

Random Forest ◽

Land Cover ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Land Cover Classification ◽

Support Vector ◽

Rural Land ◽

Object Based ◽

Vector Machines

Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8809030 ◽

2019 ◽

Author(s):

Charlotte Brabant ◽

Emilien Alvarez-Vanhard ◽

Thomas Houet

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Tree Diversity ◽

Hyperspectral Images ◽

Urban Tree ◽

Very High