[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Land cover change detection with heterogeneous remote sensing images: Review, progress, and perspective
With the fast development of remote sensing platforms and sensors technology, change
detection with heterogeneous remote sensing images (Hete-CD) has become an attractive …
detection with heterogeneous remote sensing images (Hete-CD) has become an attractive …
[HTML][HTML] A survey on deep learning-based change detection from high-resolution remote sensing images
H Jiang, M Peng, Y Zhong, H Xie, Z Hao, J Lin, X Ma… - Remote Sensing, 2022 - mdpi.com
Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …
remote sensing analysis, and it has been widely used in many areas, such as resources …
Iterative training sample augmentation for enhancing land cover change detection performance with deep learning neural network
Labeled samples are important in achieving land cover change detection (LCCD) tasks via
deep learning techniques with remote sensing images. However, labeling samples for …
deep learning techniques with remote sensing images. However, labeling samples for …
[HTML][HTML] Feature Weighted Attention—Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
In remote sensing images, change detection (CD) is required in many applications, such as:
resource management, urban expansion research, land management, and disaster …
resource management, urban expansion research, land management, and disaster …
Bi-temporal semantic reasoning for the semantic change detection in HR remote sensing images
Semantic change detection (SCD) extends the multiclass change detection (MCD) task to
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …
Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection
Deep learning for change detection is one of the current hot topics in the field of remote
sensing. However, most end-to-end networks are proposed for supervised change …
sensing. However, most end-to-end networks are proposed for supervised change …
Explainable machine learning in image classification models: An uncertainty quantification perspective
X Zhang, FTS Chan, S Mahadevan - Knowledge-Based Systems, 2022 - Elsevier
The poor explainability of deep learning models has hindered their adoption in safety and
quality-critical applications. This paper focuses on image classification models and aims to …
quality-critical applications. This paper focuses on image classification models and aims to …
Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks
Retinal fundus images provide valuable diagnostic and clinical information in the diagnosis
of ophthalmologic diseases. Retinal blood vessel analysis provides important diagnostic …
of ophthalmologic diseases. Retinal blood vessel analysis provides important diagnostic …
Concatenated deep learning framework for multi-task change detection of optical and sar images
Optical and synthetic aperture radar (SAR) images provide complementary information to
each other. However, the heterogeneity of same-ground objects brings a large difficulty to …
each other. However, the heterogeneity of same-ground objects brings a large difficulty to …