The effect of pixel resolution and spectral characteristics on the extraction of archaeological features from very high-resolution remote sensing imagery: Sagalassos, Southwest Turkey

Mapping Intimacies ◽

10.1117/12.738306 ◽

2007 ◽

Cited By ~ 1

Author(s):

V. De Laet ◽

E. Paulissen ◽

K. Meuleman ◽

M. Waelkens

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Spectral Characteristics ◽

Pixel Resolution ◽

Remote Sensing Imagery ◽

Very High

Download Full-text

Method of Improving Instance Segmentation for Very High Resolution Remote Sensing Imagery Using Deep Learning

Communications in Computer and Information Science - Data Stream Mining & Processing ◽

10.1007/978-3-030-61656-4_21 ◽

2020 ◽

pp. 323-333

Author(s):

Volodymyr Hnatushenko ◽

Vadym Zhernovyi

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Remote Sensing Imagery ◽

Very High ◽

Instance Segmentation

Download Full-text

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs8100814 ◽

2016 ◽

Vol 8 (10) ◽

pp. 814 ◽

Cited By ~ 4

Author(s):

ZhiYong Lv ◽

Haiqing He ◽

Jón Benediktsson ◽

Hong Huang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Supervised Classification ◽

Remote Sensing Imagery ◽

Very High

Download Full-text

The Potential of Deep Features for Small Object Class Identification in Very High Resolution Remote Sensing Imagery

Lecture Notes in Computer Science - Image Analysis and Recognition ◽

10.1007/978-3-319-59876-5_63 ◽

2017 ◽

pp. 569-577

Author(s):

M. Dahmane ◽

S. Foucher ◽

M. Beaulieu ◽

Y. Bouroubi ◽

M. Benoit

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Small Object ◽

Object Class ◽

Remote Sensing Imagery ◽

Class Identification ◽

Very High

Download Full-text

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Remote Sensing ◽

10.3390/rs10010144 ◽

2018 ◽

Vol 10 (1) ◽

pp. 144 ◽

Cited By ~ 114

Author(s):

Yongyang Xu ◽

Liang Wu ◽

Zhong Xie ◽

Zhanlong Chen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Building Extraction ◽

Remote Sensing Imagery ◽

Very High

Download Full-text

Segmentation of Very High Resolution Remote Sensing Imagery of Urban Areas Using Particle Swarm Optimization Algorithm

Lecture Notes in Computer Science - Image Analysis and Recognition ◽

10.1007/978-3-642-13772-3_9 ◽

2010 ◽

pp. 81-88 ◽

Cited By ~ 2

Author(s):

Safaa M. Bedawi ◽

Mohamed S. Kamel

Keyword(s):

Remote Sensing ◽

Particle Swarm Optimization ◽

High Resolution ◽

Optimization Algorithm ◽

Urban Areas ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Remote Sensing Imagery ◽

Very High

Download Full-text

CGSANet: A contour-guided and local structure-aware encoder-decoder network for accurate building extraction from very high-resolution remote sensing imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3139017 ◽

2021 ◽

pp. 1-1

Author(s):

Shanxiong Chen ◽

Wenzhong Shi ◽

Mingting Zhou ◽

Min Zhang ◽

Zhaoxin Xuan

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Local Structure ◽

Building Extraction ◽

Remote Sensing Imagery ◽

Very High

Download Full-text

Classification of Very High-Resolution Remote Sensing Imagery Using a Fully Convolutional Network With Global and Local Context Information Enhancements

IEEE Access ◽

10.1109/access.2020.2964760 ◽

2020 ◽

Vol 8 ◽

pp. 14606-14619

Author(s):

Huanjun Hu ◽

Zheng Li ◽

Lin Li ◽

Hui Yang ◽

Haihong Zhu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Local Context ◽

Context Information ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Remote Sensing Imagery ◽

Global And Local ◽

Very High

Download Full-text

Neural network and local laplace filter methods applied to very high resolution remote sensing imagery in urban damage detection

2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF) ◽

10.1109/ysf.2017.8126648 ◽

2017 ◽

Cited By ~ 4

Author(s):

D.M. Hordiiuk ◽

V.V. Hnatushenko

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Damage Detection ◽

Remote Sensing Imagery ◽

Filter Methods ◽

Very High

Download Full-text

A modified mean filter for improving the classification performance of very high-resolution remote-sensing imagery

International Journal of Remote Sensing ◽

10.1080/01431161.2017.1390275 ◽

2017 ◽

Vol 39 (3) ◽

pp. 770-785 ◽

Cited By ~ 11

Author(s):

Lv ZhiYong ◽

WenZhong Shi ◽

Jón Atli Benediktsson ◽

LiPeng Gao

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Classification Performance ◽

Remote Sensing Imagery ◽

Mean Filter ◽

Very High

Download Full-text

Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2879492 ◽

2019 ◽

Vol 16 (4) ◽

pp. 618-622 ◽

Cited By ~ 20

Author(s):

Wenqing Feng ◽

Haigang Sui ◽

Weiming Huang ◽

Chuan Xu ◽

Kaiqiang An

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Random Field ◽

Water Body ◽

Field Model ◽

Conditional Random Field ◽

Random Field Model ◽

Remote Sensing Imagery ◽

Conditional Random Field Model ◽

Very High

Download Full-text