Land-use/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery
Due to the abundant features of high spatial resolution (HSR) remote sensing images,
change detection of these images is crucial to understanding the land-use and land-cover …
change detection of these images is crucial to understanding the land-use and land-cover …
A global context-aware and batch-independent network for road extraction from VHR satellite imagery
Road extraction is to automatically label the pixels of roads in satellite imagery with specific
semantic categories based on the extraction of the topographical meaningful features. For …
semantic categories based on the extraction of the topographical meaningful features. For …
A spectral-spatial-dependent global learning framework for insufficient and imbalanced hyperspectral image classification
Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …
classification and have achieved great success. However, the deep neural network model …
Building extraction from remote sensing images with sparse token transformers
Deep learning methods have achieved considerable progress in remote sensing image
building extraction. Most building extraction methods are based on Convolutional Neural …
building extraction. Most building extraction methods are based on Convolutional Neural …
BOMSC-Net: Boundary optimization and multi-scale context awareness based building extraction from high-resolution remote sensing imagery
Automatic building extraction from high-resolution remote sensing imagery has various
applications, such as urban planning and land use management. However, the existing …
applications, such as urban planning and land use management. However, the existing …
Res2-Unet, a new deep architecture for building detection from high spatial resolution images
F Chen, N Wang, B Yu, L Wang - IEEE Journal of Selected …, 2022 - ieeexplore.ieee.org
Accurate large-scale building detection is significant in monitoring urban development, map
updating, change detection, and digital city establishment. However, due to the complicated …
updating, change detection, and digital city establishment. However, due to the complicated …
Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images
Deep learning models have brought great breakthroughs in building extraction from high-
resolution optical remote-sensing images. Among recent research, the self-attention module …
resolution optical remote-sensing images. Among recent research, the self-attention module …
Improved mask R-CNN for rural building roof type recognition from uav high-resolution images: a case study in hunan province, China
Y Wang, S Li, F Teng, Y Lin, M Wang, H Cai - Remote Sensing, 2022 - mdpi.com
Accurate roof information of buildings can be obtained from UAV high-resolution images.
The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and …
The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and …
Oil spill contextual and boundary-supervised detection network based on marine SAR images
Oil spills have caused serious harm to the marine environment. Remote sensing technology
is one of the important tools for marine environment monitoring. Synthetic aperture radar …
is one of the important tools for marine environment monitoring. Synthetic aperture radar …
[HTML][HTML] A comparative study of loss functions for road segmentation in remotely sensed road datasets
Road extraction from remote sensing imagery is a fundamental task in the field of image
semantic segmentation. For this goal, numerous supervised deep learning techniques have …
semantic segmentation. For this goal, numerous supervised deep learning techniques have …