From single-to multi-modal remote sensing imagery interpretation: A survey and taxonomy
Modality is a source or form of information. Through various modal information, humans can
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
[HTML][HTML] Enabling country-scale land cover mapping with meter-resolution satellite imagery
High-resolution satellite images can provide abundant, detailed spatial information for land
cover classification, which is particularly important for studying the complicated built …
cover classification, which is particularly important for studying the complicated built …
Cross-domain landslide mapping from large-scale remote sensing images using prototype-guided domain-aware progressive representation learning
Landslide mapping via pixel-wise classification of remote sensing imagery is essential for
hazard prevention and risk assessment. Deep-learning-based change detection greatly aids …
hazard prevention and risk assessment. Deep-learning-based change detection greatly aids …
Multi-content complementation network for salient object detection in optical remote sensing images
In the computer vision community, great progresses have been achieved in salient object
detection from natural scene images (NSI-SOD); by contrast, salient object detection in …
detection from natural scene images (NSI-SOD); by contrast, salient object detection in …
Deep Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey
L Huang, B Jiang, S Lv, Y Liu… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …
category to each pixel in remote sensing images, plays a vital role in a broad range of …
A stepwise domain adaptive segmentation network with covariate shift alleviation for remote sensing imagery
Semantic segmentation for remote sensing images (RSI) is critical for the Earth monitoring
system. However, the covariate shift between RSI datasets under different capture …
system. However, the covariate shift between RSI datasets under different capture …
[HTML][HTML] DPENet: Dual-path extraction network based on CNN and transformer for accurate building and road extraction
The acceleration of urbanization and the increasing demand for precise city planning have
made the extraction of buildings and roads from remote sensing images crucial. Deep …
made the extraction of buildings and roads from remote sensing images crucial. Deep …
[HTML][HTML] A 2D/3D multimodal data simulation approach with applications on urban semantic segmentation, building extraction and change detection
Advances in remote sensing image processing techniques have further increased the
demand for annotated datasets. However, preparing annotated multi-temporal 2D/3D …
demand for annotated datasets. However, preparing annotated multi-temporal 2D/3D …
Federated deep learning with prototype matching for object extraction from very-high-resolution remote sensing images
Deep convolutional neural networks (DCNNs) have become the leading tools for object
extraction from very-high-resolution (VHR) remote sensing images. However, the label …
extraction from very-high-resolution (VHR) remote sensing images. However, the label …
Semi-supervised building footprint generation with feature and output consistency training
Accurate and reliable building footprint maps are vital to urban planning and monitoring, and
most existing approaches fall back on convolutional neural networks (CNNs) for building …
most existing approaches fall back on convolutional neural networks (CNNs) for building …