A Self-Supervised Learning Method for Shadow Detection in Remote Sensing Imagery

3D Research ◽  
2018 ◽  
Vol 9 (4) ◽  
Author(s):  
Shoulin Yin ◽  
Jie Liu ◽  
Hang Li
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Shadow Detection ◽  
Learning Method ◽  
Remote Sensing Imagery

The Object-based Method of Shadow Detection in High-resolution Remote Sensing Imagery

2015 ◽  
Vol 44 (12) ◽  
pp. 1228002 ◽  
Author(s):  
帅滔 SHUAI Tao ◽  
张洪艳 ZHANG Hong-yan ◽  
张良培 ZHANG Liang-pei
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Shadow Detection ◽  
Remote Sensing Imagery ◽  
Object Based

scholarly journals Early Labeled and Small Loss Selection Semi-Supervised Learning Method for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (20) ◽  
pp. 4039
Author(s):  
Ye Tian ◽  
Yuxin Dong ◽  
Guisheng Yin
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Early Stage ◽  
Remote Sensing Image ◽  
Learning Method ◽  
Scene Classification ◽  
Small Loss ◽  
Basic Work ◽  
Spatial Differences ◽  
Remote Sensing Image Processing

The classification of aerial scenes has been extensively studied as the basic work of remote sensing image processing and interpretation. However, the performance of remote sensing image scene classification based on deep neural networks is limited by the number of labeled samples. In order to alleviate the demand for massive labeled samples, various methods have been proposed to apply semi-supervised learning to train the classifier using labeled and unlabeled samples. However, considering the complex contextual relationship and huge spatial differences, the existing semi-supervised learning methods bring different degrees of incorrectly labeled samples when pseudo-labeling unlabeled data. In particular, when the number of labeled samples is small, it affects the generalization performance of the model. In this article, we propose a novel semi-supervised learning method with early labeled and small loss selection. First, the model learns the characteristics of simple samples in the early stage and uses multiple early models to screen out a small number of unlabeled samples for pseudo-labeling based on this characteristic. Then, the model is trained in a semi-supervised manner by combining labeled samples, pseudo-labeled samples, and unlabeled samples. In the training process of the model, small loss selection is used to further eliminate some of the noisy labeled samples to improve the recognition accuracy of the model. Finally, in order to verify the effectiveness of the proposed method, it is compared with several state-of-the-art semi-supervised classification methods. The results show that when there are only a few labeled samples in remote sensing image scene classification, our method is always better than previous methods.

A Deep Learning Method for Ocean Front Extraction in Remote Sensing Imagery

2021 ◽  
pp. 1-5
Author(s):  
Yangdong Li ◽  
Junhao Liang ◽  
Hengrong Da ◽  
Liang Chang ◽  
Hongli Li
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Learning Method ◽  
Remote Sensing Imagery ◽  
Ocean Front

The research on the shadow detection from high resolution remote sensing imagery

2013 ◽  
Author(s):  
Zhong Chen ◽  
Heng Zhou ◽  
Tao Deng ◽  
Song Luo
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Shadow Detection ◽  
Remote Sensing Imagery

Semi-supervised learning of heterogeneous data in remote sensing imagery

2012 ◽  
Author(s):  
J. Benedetto ◽  
W. Czaja ◽  
J. Dobrosotskaya ◽  
T. Doster ◽  
K. Duke ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Heterogeneous Data ◽  
Remote Sensing Imagery

scholarly journals MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (16) ◽  
pp. 3122
Author(s):  
Bo Dang ◽  
Yansheng Li
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Water Body ◽  
Water Resource Management ◽  
Unlabeled Data ◽  
Water Bodies ◽  
Residual Network ◽  
Research Attention ◽  
Remote Sensing Imagery ◽  
Practical Applications

Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.

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