Results 1 to 10 of about 16,201 (241)

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

open access: yesRemote Sensing, 2020
In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole ...
Ronghua Shang   +2 more
exaly   +4 more sources

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

open access: yesRemote 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, Licheng Jiao, Yanqiao Chen
exaly   +4 more sources

Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels

open access: yesRemote 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   +4 more sources

Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

open access: yesRemote Sensing, 2020
Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification.
Yuwei Guo, Rong Qu, Licheng Jiao
exaly   +3 more sources

Purifying SLIC Superpixels to Optimize Superpixel-Based Classification of High Spatial Resolution Remote Sensing Image

open access: yesRemote Sensing, 2019
Fast and accurate classification of high spatial resolution remote sensing image is important for many applications. The usage of superpixels in classification has been proposed to accelerate the speed of classification.
Hengjian Tong, Fei Tong, Yun Zhang
exaly   +3 more sources

Hypergraph Convolution Network Classification for Hyperspectral and LiDAR Data [PDF]

open access: yesSensors
Conventional 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

Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification

open access: yesRemote 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

Local and Global Spatial Information for Land Cover Semisupervised Classification of Complex Polarimetric SAR Data

open access: yesIEEE 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

Semantic-Aware Region Loss for Land-Cover Classification

open access: yesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Integrating superpixel segmentation into convolutional neural networks is known to be effective in enhancing the accuracy of land-cover classification.
Xianwei Zheng   +5 more
doaj   +1 more source

A Superpixel Boundary Optimization (SBO) Framework Based on Information Measure Function

open access: yesIEEE Access, 2020
Superpixel is an essential tool for computer vision. In practice, classic superpixel algorithms do not exhibit good boundary adherence with fewer superpixels, which will greatly hamper further analysis.
Guoqi Liu   +3 more
doaj   +1 more source

Home - About - Disclaimer - Privacy