☆ 4.7 Article

Joint Learning of Semantic Segmentation and Height Estimation for Remote Sensing Image Leveraging Contrastive Learning

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2023)

Related references

Note: Only part of the references are listed.

Article Geochemistry & Geophysics

Gated Feature Aggregation for Height Estimation From Single Aerial Images

Siyuan Xing et al.

Summary: A progressive learning network is proposed in this study, aiming to estimate height information from single aerial images by combining low-level and high-level features. Experimental results demonstrate that the proposed method can achieve more accurate height estimation, especially with better object boundary and contour preserving capability, compared to other methods.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Add to Collection

Article Geochemistry & Geophysics

A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images

Libo Wang et al.

Summary: The fully convolutional network (FCN) with an encoder-decoder architecture is widely used for semantic segmentation. In this paper, the authors propose using the Swin Transformer as the backbone and a novel decoder called DCFAM for better context extraction and resolution restoration. Experimental results on two remotely sensed semantic segmentation datasets demonstrate the effectiveness of the proposed scheme.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Add to Collection

Article Geochemistry & Geophysics

Height Estimation From Single Aerial Images Using a Deep Ordinal Regression Network

Xiang Li et al.

Summary: Understanding the 3-D geometric structure of the Earth's surface is crucial for various applications. Previous research focused on height estimation from multiple images, while this letter tackles the problem of height estimation from a single aerial image. By using deep learning and an ordinal loss, this method outperforms state-of-the-art techniques.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Add to Collection

Article Geochemistry & Geophysics

Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery

Cheng Zhang et al.

Summary: This article presents a hybrid deep neural network that combines transformer and convolutional neural network (CNN) for semantic segmentation of very high resolution remote sensing imagery. The network utilizes a new universal backbone Swin transformer for feature extraction and incorporates various strategies for multiscale context modeling. It achieves improved accuracy through skip connections and an auxiliary boundary detection branch.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

Asymmetric Hash Code Learning for Remote Sensing Image Retrieval

Weiwei Song et al.

Summary: This article proposes a novel deep hashing method called asymmetric hash code learning (AHCL) for remote sensing image retrieval (RSIR). The proposed method generates the hash codes of query and database images in an asymmetric way and improves the representation ability of deep features and hash codes by combining semantic information and similarity information. Experimental results show that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

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

Haifeng Li et al.

Summary: This study proposes a global style and local matching contrastive learning network (GLCNet) for remote sensing image (RSI) semantic segmentation. By using global style contrastive learning and local feature matching contrastive learning modules, the method achieves superior results compared to state-of-the-art methods and supervised learning methods on various RSI semantic segmentation datasets.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

Collaborative Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images

Qian Zhang et al.

Summary: In this work, a collaborative network is designed to simultaneously solve the super-resolution semantic segmentation and super-resolution image reconstruction tasks using a simple and efficient multitask learning algorithm. Experimental results showed that the proposed method achieved more accurate semantic segmentation and super-resolution reconstruction results even when the input image resolution was reduced by half on the Potsdam dataset.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Add to Collection

Article Environmental Sciences

SCE-Net: Self- and Cross-Enhancement Network for Single-View Height Estimation and Semantic Segmentation

Siyuan Xing et al.

Summary: In this paper, a network called SCE-Net is proposed for single aerial image height estimation and semantic segmentation. By using a feature separation-fusion module and feature distance loss, the method effectively extracts and fuses height features and semantic features, achieving task-aware feature representation enhancement. Experimental results demonstrate that the proposed method outperforms existing methods in both height estimation and semantic segmentation.

REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

Associatively Segmenting Semantics and Estimating Height From Monocular Remote-Sensing Imagery

Wenjie Liu et al.

Summary: This article introduces a novel multitask learning method for associatively segmenting semantics and estimating height from remote-sensing imagery. By incorporating task-specific distillation and cross-task propagation modules, as well as a dynamic weighted geometric mean strategy, it addresses technical limitations in semantic segmentation and height estimation.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images

Weiwei Song et al.

Summary: The article introduces a novel deep hashing convolutional neural network (DHCNN) for simultaneous image retrieval and classification, achieving state-of-art performance in both aspects.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Deep High-Resolution Representation Learning for Visual Recognition

Jingdong Wang et al.

Summary: The High-Resolution Network (HRNet) maintains high-resolution representations and exchanges information across resolutions, resulting in superior performance in various applications such as human pose estimation, semantic segmentation, and object detection.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Add to Collection

Article Geochemistry & Geophysics

Soft-Aligned Gradient-Chaining Network for Height Estimation From Single Aerial Images

Donglin Mo et al.

Summary: By utilizing FCN networks and elevation gradient chaining with the surface alignment method, this research successfully tackled the problems of label-output correlation and adjacent pixel height correlation in single-image elevation estimations, leading to significant improvements in quantitative and visual evaluations.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

Add to Collection

Article Environmental Sciences

Photogrammetric Digital Surface Model Reconstruction in Extreme Low-Light Environments

Riccardo Roncella et al.

Summary: The study demonstrates that modern high-sensitivity cameras enable accurate DSM reconstruction in extreme low-light environments and, with the correct camera setup, achieve comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.

REMOTE SENSING (2021)

Add to Collection

Article Geochemistry & Geophysics

U-IMG2DSM: Unpaired Simulation of Digital Surface Models With Generative Adversarial Networks

M. E. Paoletti et al.

Summary: This paper introduces a new deep learning approach based on VAEs and GANs to obtain DSMs from optical images, demonstrating improved classification results in remote sensing applications.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

Add to Collection

Article Engineering, Electrical & Electronic

Boundary-Aware Multitask Learning for Remote Sensing Imagery

Yufeng Wang et al.

Summary: This paper proposes a boundary-aware multitask learning framework for semantic segmentation, height estimation, and boundary detection tasks, improving model performance by introducing a boundary attentive module and a boundary regularized loss term.

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

Add to Collection

Article Computer Science, Artificial Intelligence

A Supervised Segmentation Network for Hyperspectral Image Classification

Hao Sun et al.

Summary: In this study, an end-to-end fully convolutional segmentation network (FCSN) is proposed to simultaneously identify land-cover labels of all pixels in a HSI cube. The study also introduces a fine label style to label all pixels of HSI cubes for detailed spatial land-cover distributions and a HSI cube generation method to improve the diversity of spatial land-cover distributions. Experimental results demonstrate that FCSN has superior generalization capabilities to the changes of spatial land-cover distributions.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

Add to Collection

Article Geochemistry & Geophysics

Remote Sensing Scene Classification by Gated Bidirectional Network

Hao Sun et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2020)

Add to Collection

Article Geochemistry & Geophysics

Multitask Learning of Height and Semantics From Aerial Images

Marcela Carvalho et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2020)

Add to Collection

Article Environmental Sciences

Generating Elevation Surface from a Single RGB Remotely Sensed Image Using Deep Learning

Emmanouil Panagiotou et al.

REMOTE SENSING (2020)

Add to Collection

Article Environmental Sciences

IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery

Chao-Jung Liu et al.

REMOTE SENSING (2020)

Add to Collection

Article Geography, Physical

Height estimation from single aerial images using a deep convolutional encoder-decoder network

Hamed Amini Amirkolaee et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2019)

Add to Collection

Article Environmental Sciences

A New Single-Pass SAR Interferometry Technique with a Single-Antenna for Terrain Height Measurements

Min-Ho Ka et al.

REMOTE SENSING (2019)

Add to Collection

Article Geochemistry & Geophysics

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Add to Collection

Article Environmental Sciences

2D Image-To-3D Model: Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)

Fatemeh Alidoost et al.

REMOTE SENSING (2019)

Add to Collection

Review Multidisciplinary Sciences

Deep Metric Learning: A Survey

Mahmut Kaya et al.

SYMMETRY-BASEL (2019)

Add to Collection

Article Geochemistry & Geophysics

Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks

Keiller Nogueira et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Add to Collection

Proceedings Paper Computer Science, Hardware & Architecture

MULTI-PATH FUSION NETWORK FOR HIGH-RESOLUTION HEIGHT ESTIMATION FROM A SINGLE ORTHOPHOTO

Yiteng Zhang et al.

2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW) (2019)

Add to Collection

Proceedings Paper Geosciences, Multidisciplinary

POP-NET: ENCODER-DUAL DECODER FOR SEMANTIC SEGMENTATION AND SINGLE-VIEW HEIGHT ESTIMATION

Zhuo Zheng et al.

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) (2019)

Add to Collection

Article Geochemistry & Geophysics

IMG2DSM: Height Simulation From Single Imagery Using Conditional Generative Adversarial Net

Pedram Ghamisi et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)

Add to Collection

Article Geochemistry & Geophysics

Generation of Highly Accurate DEMs Over Flat Areas by Means of Dual-Frequency and Dual-Baseline Airborne SAR Interferometry

Muriel Pinheiro et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Add to Collection

Article Geochemistry & Geophysics

Hyperspectral Image Classification With Deep Feature Fusion Network

Weiwei Song et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2018)

Add to Collection

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)

Add to Collection

Article Computer Science, Artificial Intelligence

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2017)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

BlitzNet: A Real-Time Deep Network for Scene Understanding

Nikita Dvornik et al.

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

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Mask R-CNN

Kaiming He et al.

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

Add to Collection

Article Computer Science, Artificial Intelligence

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Alexey Dosovitskiy et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2016)

Add to Collection

Article Computer Science, Artificial Intelligence

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Fast R-CNN

Ross Girshick

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

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture

David Eigen et al.

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

Add to Collection

© Peeref 2019-2024. All rights reserved.