Satellite images taken on the earth's surface are analyzed to identify the spatial and temporal changes that have occurred naturally or manmade. Real-time prediction of change …
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image …
Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed …
Y Feng, H Xu, J Jiang, H Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Change detection (CD) of remote sensing (RS) images has enjoyed remarkable success by virtue of convolutional neural networks (CNNs) with promising discriminative capabilities …
K Yang, GS Xia, Z Liu, B Du, W Yang… - … on Geoscience and …, 2021 - ieeexplore.ieee.org
Given two multitemporal aerial images, semantic change detection (SCD) aims to locate the land-cover variations and identify their change types with pixelwise boundaries. This …
Building change detection (BCD) recently can be handled well under the booming of deep- learning based computer vision techniques. However, segmentation and recognition for …
Z Lv, H Huang, W Sun, M Jia… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
Labeled samples are important in achieving land cover change detection (LCCD) tasks via deep learning techniques with remote sensing images. However, labeling samples for …
With the acceleration of urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial …
H Chen, W Li, Z Shi - IEEE Transactions on Geoscience and …, 2021 - ieeexplore.ieee.org
Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale …