Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives
Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …
classifying whether each pixel of the image is water or not, has become a hot issue in the …
Fast and robust matching for multimodal remote sensing image registration
While image matching has been studied in remote sensing community for decades,
matching multimodal data [eg, optical, light detection and ranging (LiDAR), synthetic …
matching multimodal data [eg, optical, light detection and ranging (LiDAR), synthetic …
[HTML][HTML] Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification
In this paper, we aim at tackling a general but interesting cross-modality feature learning
question in remote sensing community—can a limited amount of highly-discriminative (eg …
question in remote sensing community—can a limited amount of highly-discriminative (eg …
[HTML][HTML] Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction
Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-
level data analysis, has been garnering growing attention in the remote sensing community …
level data analysis, has been garnering growing attention in the remote sensing community …
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …
Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution
Landuse characterization is important for urban planning. It is traditionally performed with
field surveys or manual photo interpretation, two practices that are time-consuming and …
field surveys or manual photo interpretation, two practices that are time-consuming and …
Unsupervised image regression for heterogeneous change detection
Change detection in heterogeneous multitemporal satellite images is an emerging and
challenging topic in remote sensing. In particular, one of the main challenges is to tackle the …
challenging topic in remote sensing. In particular, one of the main challenges is to tackle the …
Spectral–spatial weighted kernel manifold embedded distribution alignment for remote sensing image classification
Feature distortions of data are a typical problem in remote sensing image classification,
especially in the area of transfer learning. In addition, many transfer learning-based methods …
especially in the area of transfer learning. In addition, many transfer learning-based methods …
Few-shot incremental learning with continual prototype calibration for remote sensing image fine-grained classification
Z Zhu, P Wang, W Diao, J Yang, H Wang… - ISPRS Journal of …, 2023 - Elsevier
With the rapid acquisition of remote sensing (RS) data, new categories of objects continue to
emerge, and some categories can only obtain a few training samples. Thus, few-shot class …
emerge, and some categories can only obtain a few training samples. Thus, few-shot class …
[HTML][HTML] An assessment approach for pixel-based image composites
Remote sensing is one of the main sources of information for monitoring forest dynamics;
however, surface reflectance is often not possible to accurately derive due to haze, cloud, or …
however, surface reflectance is often not possible to accurately derive due to haze, cloud, or …