4.7 Article

Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Geochemistry & Geophysics

Remote Sensing Image Scene Classification With Self-Supervised Paradigm Under Limited Labeled Samples

Chao Tao et al.

Summary: With the development of deep learning, supervised learning methods have shown good performance in remote sensing image scene classification. However, these methods require a large amount of labeled data for training. In this study, a new self-supervised learning mechanism is introduced to obtain high-performance pretraining models for scene classification from large unlabeled data. Experiments demonstrate that this new paradigm outperforms traditional ImageNet pretrained models, and the insights obtained can contribute to the development of self-supervised learning in the remote sensing community.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Geochemistry & Geophysics

Contrastive Self-Supervised Learning With Smoothed Representation for Remote Sensing

Heechul Jung et al.

Summary: This letter introduces a contrastive self-supervised learning technique based on the SimCLR framework, which utilizes smoothed representation to enhance recognition in remote sensing.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Geochemistry & Geophysics

Lie to Me: A Soft Threshold Defense Method for Adversarial Examples of Remote Sensing Images

Li Chen et al.

Summary: Adversarial examples generated through perturbations deceive models into predicting incorrect results, showcasing the vulnerability of CNNs. Research indicates that adversarial example attacks are prevalent in remote sensing image scene classification, exhibiting selectivity in misclassification. A proposed soft threshold defense method efficiently distinguishes adversarial examples based on decision boundaries, reducing fooling rates significantly in various attack scenarios.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Computer Science, Artificial Intelligence

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

Longlong Jing et al.

Summary: This paper reviews deep learning-based self-supervised general visual feature learning methods, covering motivation, pipeline, architectures, schema, evaluation metrics, datasets, performance comparisons, and future directions.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Geochemistry & Geophysics

AFNet: Adaptive Fusion Network for Remote Sensing Image Semantic Segmentation

Rui Liu et al.

Summary: A novel adaptive fusion network (AFNet) is proposed to improve the performance of very high resolution (VHR) remote sensing image segmentation, utilizing scale-feature attention module (SFAM) and scale-layer attention module (SLAM) in a multilevel architecture. Extensive experiments demonstrate the effectiveness of the proposed model.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Geochemistry & Geophysics

An Empirical Study of Adversarial Examples on Remote Sensing Image Scene Classification

Li Chen et al.

Summary: This study tested eight state-of-the-art classification DNNs on six RSI benchmarks and found that adversarial examples can impact remote sensing image scene classification. The seriousness of the adversarial problem in optical data has a negative relationship with the richness of the feature information, while adversarial examples generated from SAR images can easily fool models.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Geochemistry & Geophysics

Deep Multiview Learning for Hyperspectral Image Classification

Bing Liu et al.

Summary: This article proposes a deep multiview learning method to address the small sample problem in hyperspectral image classification. By constructing multiple views and designing a deep residual network, the method improves classification accuracy in an unsupervised learning setting.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Geochemistry & Geophysics

RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification

Haifeng Li et al.

Summary: Training modern deep neural networks on large labeled datasets is effective for scene classification, but learning from a few data points remains challenging. RS-MetaNet proposes a method of organizing training at the task level, achieving state-of-the-art results in few-shot remote sensing scene classification.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Geochemistry & Geophysics

MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery

Qing Zhu et al.

Summary: Building footprint extraction is a fundamental task in various fields such as mapping and computer vision, but current CNN-based methods struggle with detecting tiny buildings and inaccurate segmentation of large buildings. Recent research has introduced multiscale strategies and a novel neural network structure to improve the efficiency and accuracy of building footprint extraction.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Review Engineering, Multidisciplinary

A Survey on Contrastive Self-Supervised Learning

Ashish Jaiswal et al.

Summary: Self-supervised learning, particularly through contrastive learning, has gained popularity for its cost-effective approach in using self-defined pseudolabels for various downstream tasks. This paper extensively reviews self-supervised methods following the contrastive approach, explaining pretext tasks and different architectures used. Performance comparisons across multiple downstream tasks demonstrate variations in method effectiveness.

TECHNOLOGIES (2021)

Article Engineering, Electrical & Electronic

Adversarial Examples for CNN-Based SAR Image Classification: An Experience Study

Haifeng Li et al.

Summary: The study utilizes white-box attack methods to generate adversarial examples of SAR images, revealing vulnerabilities of different CNNs when facing AEs. The research shows that ASIs are effective in fooling trained CNNs, exhibiting different vulnerabilities with high attack success rates. By analyzing the parameter sensitivity, the study highlights the impact of image parameters on the attack success rate of ASIs.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

Article Engineering, Electrical & Electronic

DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images

Jie Chen et al.

Summary: Change detection is a fundamental task in remote sensing image processing. While deep learning has provided new tools for this task, current methods lack resistance to pseudochange information. To address this issue, a new method called dual attentive fully convolutional Siamese networks is proposed, achieving significant improvements compared to baseline methods according to experimental results.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

Article Geochemistry & Geophysics

LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images

Lei Ding et al.

Summary: Two proposed modules, Patch Attention Module (PAM) and Attention Embedding Module (AEM), enhance feature representation in remote sensing images by bridging the gap between high-level and low-level features. Experimental results show that integrating these modules into a baseline fully convolutional network greatly improves performance and outperforms other attention-based methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Review Environmental Sciences

Remote sensing for agricultural applications: A meta-review

M. Weiss et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Environmental Sciences

Deep learning in environmental remote sensing: Achievements and challenges

Qiangqiang Yuan et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Geochemistry & Geophysics

Transfer Learning for SAR Image Classification via Deep Joint Distribution Adaptation Networks

Jie Geng et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Geochemistry & Geophysics

Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture

Lei Ding et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Article Environmental Sciences

Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network

Francois Waldner et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Geography, Physical

A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem

Fariba Mohammadimanesh et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

Proceedings Paper Computer Science, Artificial Intelligence

DADA: Depth-Aware Domain Adaptation in Semantic Segmentation

Tuan-Hung Vu et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Article Computer Science, Artificial Intelligence

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Liang-Chieh Chen et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Article Environmental Sciences

Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment

Guy J-P. Schumann et al.

REMOTE SENSING (2018)

Proceedings Paper Computer Science, Theory & Methods

Improving the Segmentation of Anatomical Structures in Chest Radiographs Using U-Net with an ImageNet Pre-trained Encoder

Maayan Frid-Adar et al.

IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Xun Huang et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised Visual Representation Learning by Context Prediction

Carl Doersch et al.

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

Article Remote Sensing

Support vector machines for classification in remote sensing

M Pal et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2005)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)