Results 11 to 20 of about 3,600 (211)
Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels
Remote Sensing, 2019 Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets ...
Yangyang Li +2 more
exaly +3 more sources
Hypergraph Convolution Network Classification for Hyperspectral and LiDAR Data [PDF]
SensorsConventional remote sensing classification approaches based on single-source data exhibit inherent limitations, driving significant research interest in improved multimodal data fusion techniques.
Lei Wang, Shiwen Deng
doaj +2 more sources
Over the years, the use of superpixel segmentation has become very popular in various applications, serving as a preprocessing step to reduce data size by adapting to the content of the image, regardless of its semantic content.
Clément, Michaël, Giraud, Rémi
core +2 more sources
Automatic Image Segmentation With Superpixels and Image-Level Labels
IEEE Access, 2019 Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing.
Xinlin Xie +4 more
doaj +2 more sources
, 2013 We propose and evaluate a versatile scheme for image pre-segmentation that generates a partition of the image into a selectable number of patches (’superpixels’), under the constraint of obtaining maximum homogeneity of the ’texture’ inside of each patch,
Christian Conrad +5 more
core +2 more sources
Remote Sensing, 2017 Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the ...
Ovidiu Csillik
exaly +3 more sources
Remote Sensing, 2023 Superpixel-based classification using Active Learning (AL) has shown great potential in high spatial resolution remote sensing image classification tasks.
Jiechen Tang +4 more
doaj +1 more source
Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments. [PDF]
SmallThis study presents an interpretable, lightweight hybrid deep learning model for real‐time analysis of breast cancer histopathology in IoMT‐enabled diagnostic systems. By integrating MobileNetV2 and EfficientNet‐B0 with a novel contextual recurrent attention module (CRAM), the framework achieves near‐perfect accuracy while providing transparent Grad ...
Ogundokun RO +4 more
europepmc +2 more sources
TSSP-UNet: A Two-Stage Weakly Supervised Pathological Image Segmentation With Point Annotations. [PDF]
IET Syst BiolDeep convolutional neural networks excel at image segmentation but face challenges with complex instance training and high‐precision annotation acquisition. This study proposes TSSP‐UNet, a two‐stage weakly supervised segmentation approach: the first stage trains a segmentation network with constraint and attention mechanisms plus a feature aggregation
Wang S +5 more
europepmc +2 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Each of the three satellites constituting the RADARSAT Constellation Mission (RCM) provides compact polarimetric synthetic aperture radar (CP SAR) data.
Mohsen Ghanbari +2 more
doaj +1 more source