skip to main content
Idioma:
Resultados 1 2 3 4 5 next page
Refinado por: Base de dados/Biblioteca: EZB Electronic Journals Library remover Nome da Publicação: Remote Sensing remover
Result Number Material Type Add to My Shelf Action Record Details and Options
1
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification
Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification
Material Type:
Artigo 		Adicionar ao Meu Espaço

Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification

Li, Yong ; Shao, Zhenfeng ; Huang, Xiao ; Cai, Bowen ; Peng, Song

Remote sensing (Basel, Switzerland), 2021-07, Vol.13 (14), p.2776 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
2
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
Material Type:
Artigo 		Adicionar ao Meu Espaço

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Ghorbanzadeh, Omid ; Blaschke, Thomas ; Gholamnia, Khalil ; Meena, Sansar ; Tiede, Dirk ; Aryal, Jagannath

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
3
Self-Supervision and Self-Distillation with Multilayer Feature Contrast for Supervision Collapse in Few-Shot Remote Sensing Scene Classification
Self-Supervision and Self-Distillation with Multilayer Feature Contrast for Supervision Collapse in Few-Shot Remote Sensing Scene Classification
Material Type:
Artigo 		Adicionar ao Meu Espaço

Self-Supervision and Self-Distillation with Multilayer Feature Contrast for Supervision Collapse in Few-Shot Remote Sensing Scene Classification

Zhou, Haonan ; Du, Xiaoping ; Li, Sen

Remote sensing (Basel, Switzerland), 2022-07, Vol.14 (13), p.3111 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
4
Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images
Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images
Material Type:
Artigo 		Adicionar ao Meu Espaço

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

Fang, Bo ; Pan, Li ; Kou, Rong

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1292 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
5
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
Material Type:
Artigo 		Adicionar ao Meu Espaço

Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

Taghizadeh-Mehrjardi, Ruhollah ; Schmidt, Karsten ; Amirian-Chakan, Alireza ; Rentschler, Tobias ; Zeraatpisheh, Mojtaba ; Sarmadian, Fereydoon ; Valavi, Roozbeh ; Davatgar, Naser ; Behrens, Thorsten ; Scholten, Thomas

Remote sensing (Basel, Switzerland), 2020-04, Vol.12 (7), p.1095 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
6
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
Material Type:
Artigo 		Adicionar ao Meu Espaço

Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

Vali, Ava ; Comai, Sara ; Matteucci, Matteo

Remote sensing (Basel, Switzerland), 2020-08, Vol.12 (15), p.2495 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
7
PM[sub.2.5] Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network
PM[sub.2.5] Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network
Material Type:
Artigo 		Adicionar ao Meu Espaço

PM[sub.2.5] Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network

Wang, Junwei ; Gao, Kun ; Hu, Xiuqing ; Zhang, Xiaodian ; Wang, Hong ; Hu, Zibo ; Yang, Zhijia ; Zhang, Peng

Remote sensing (Basel, Switzerland), 2023-10, Vol.15 (20) [Periódico revisado por pares]

MDPI AG

Texto completo disponível

Citações Citado por
8
Few-Shot Classification of Aerial Scene Images via Meta-Learning
Few-Shot Classification of Aerial Scene Images via Meta-Learning
Material Type:
Artigo 		Adicionar ao Meu Espaço

Few-Shot Classification of Aerial Scene Images via Meta-Learning

Zhang, Pei ; Bai, Yunpeng ; Wang, Dong ; Bai, Bendu ; Li, Ying

Remote sensing (Basel, Switzerland), 2021-01, Vol.13 (1), p.108 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
9
A Review of Deep Learning in Multiscale Agricultural Sensing
A Review of Deep Learning in Multiscale Agricultural Sensing
Material Type:
Artigo 		Adicionar ao Meu Espaço

A Review of Deep Learning in Multiscale Agricultural Sensing

Wang, Dashuai ; Cao, Wujing ; Zhang, Fan ; Li, Zhuolin ; Xu, Sheng ; Wu, Xinyu

Remote sensing (Basel, Switzerland), 2022-02, Vol.14 (3), p.559 [Periódico revisado por pares]

Basel: MDPI AG

Texto completo disponível

Citações Citado por
10
How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?
How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?
Material Type:
Artigo 		Adicionar ao Meu Espaço

How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?

Tian, Zhongyan ; Wei, Jing ; Li, Zhanqing

Remote sensing (Basel, Switzerland), 2023-07, Vol.15 (15) [Periódico revisado por pares]

MDPI AG

Texto completo disponível

Citações Citado por
Resultados 1 2 3 4 5 next page

Personalize Seus Resultados

  1. Editar

Refine Search Results

Expandir Meus Resultados

  1.   

Data de Publicação 

De até
Refinar
  1. Antes de2013  (7)
  2. 2013Até2014  (15)
  3. 2015Até2016  (55)
  4. 2017Até2019  (880)
  5. Após 2019  (6.563)
  6. Mais opções open sub menu

Idioma 

  1. Japonês  (338)
  2. Norueguês  (4)
  3. Mais opções open sub menu

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.