Wangbin Li
YOU?
Author Swipe
Adapting Cross-Sensor High-Resolution Remote Sensing Imagery for Land Use Classification Open
High-resolution visible remote sensing imagery, as a fundamental contributor to Earth observation, has found extensive application in land use classification. However, the heterogeneous array of optical sensors, distinguished by their uniq…
Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning Open
Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual…
View article: Deep Merge: Deep-Learning-Based Region Merging for Remote Sensing Image Segmentation
Deep Merge: Deep-Learning-Based Region Merging for Remote Sensing Image Segmentation Open
Image segmentation represents a fundamental step in analyzing very high-spatial-resolution (VHR) remote sensing imagery. Its objective is to partition an image into segments that best match with geo-objects. However, the diverse appearance…
YOLO-AFP: A More Robust Network for Aerial Object Detection Open
In practical applications of aerial object detection, real-time uncrewed aerial vehicle (UAV) imagery is often affected by noise, low light, and cloud occlusion, leading to poor image quality. The performance of mainstream UAV object detec…
On-Orbit Radiometric Performance Assessment of FY-3F MERSI-III: A Cross-Platform Intercomparison Approach Based on Global Pseudo-Invariant Pixels Open
FengYun-3F, a new generation Chinese polar-orbiting meteorological satellite, is equipped with the advanced Medium Resolution Spectral Imager-3 (MERSI-III). Extensive validation tests are currently underway to characterize on-orbit capabil…
WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection Open
As an inherent characteristic of synthetic aperture radar (SAR) systems, the presence of speckle noise reduces the signal-to-noise ratio (SNR) of SAR images, leading to blurred image details and limiting the accuracy of change detection in…
View article: A building change detection framework with patch-pairing single-temporal supervised learning and metric guided attention mechanism
A building change detection framework with patch-pairing single-temporal supervised learning and metric guided attention mechanism Open
Building change detection (CD) aims to detect changes in buildings from bi-temporal pairwise images obtained at different times. Typically, a deep learning-based building CD algorithm requires bi-temporal samples with significant building …
Assisted learning for land use classification: The important role of semantic correlation between heterogeneous images Open
In recent times, notable advancements have been achieved in amalgamating heterogeneous remote sensing imagery to facilitate Earth observation through the adoption of convolutional neural networks. Nonetheless, due to the variety in imaging…
Aligning semantic distribution in fusing optical and SAR images for land use classification Open
Optical and synthetic aperture radar (SAR) images, two standard Earth observation tools, can reflect the characteristics of the surface from different perspectives and provide complementary information for land use classification. However,…
Extracting buildings from high-resolution remote sensing images by deep ConvNets equipped with structural-cue-guided feature alignment Open
In surveying, mapping and geographic information systems, building extraction from remote sensing imagery is a common task. However, there are still some challenges in automatic building extraction. First, using only single-scale depth fea…
Built-Up Area Change Detection Using Multi-Task Network with Object-Level Refinement Open
The detection and monitoring of changes in urban buildings, as a major place for human activities, have been considered profoundly in the field of remote sensing. In recent years, comparing with other traditional methods, the deep learning…
PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information Open
Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activitie…