Results 61 to 70 of about 471,167 (221)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial-intelligence-guided tasks, such as land cover mapping and ground-object mapping, most deep-learning-based architectures
T. Behera +3 more
semanticscholar +1 more source
Improved Spatial-Spectral Superpixel Hyperspectral Unmixing
Remote Sensing, 2019 In this paper, an unsupervised unmixing approach based on superpixel representation combined with regional partitioning is presented. A reduced-size image representation is obtained using superpixel segmentation where each superpixel is represented by ...
Mohammed Q. Alkhatib +1 more
doaj +1 more source
Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often
Ronghua Shang +4 more
doaj +1 more source
Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing
European Journal of Remote Sensing, 2022 Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of ...
Qiang Guan +4 more
doaj +1 more source
Joint Optical Flow and Temporally Consistent Semantic Segmentation
, 2016 The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems. Consequently, there has been a large amount of research dedicated to semantic segmentation and dense motion ...
A Kundu +16 more
core +1 more source
Semantic-Aware Superpixel for Weakly Supervised Semantic Segmentation
AAAI Conference on Artificial Intelligence, 2023 Weakly-supervised semantic segmentation aims to train a semantic segmentation network using weak labels. Among weak labels, image-level label has been the most popular choice due to its simplicity.
Sangtae Kim, Daeyoung Park, B. Shim
semanticscholar +1 more source
Automatic inventory of retaining walls from aerial lidar data using 3D deep learning
Civil Engineering Design, Volume 7, Issue 4, Page 178-189, December 2025.Abstract Infrastructure management along highways and railways requires inventories of critical structures like retaining walls, which traditionally rely on manual inspection and documentation. Unfortunately, data in infrastructure databases is often incomplete.
Ivo Gasparini +2 more
wiley +1 more source
A survey on the utilization of Superpixel image for clustering based image segmentation
Multimedia tools and applications, 2023 Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent.
Buddhadev Sasmal, K. G. Dhal
semanticscholar +1 more source
Engineering Reports, Volume 7, Issue 12, December 2025.A custom deep learning model with explainable artificial intelligence for interpretable brain tumor classification. ABSTRACT Brain tumors are critical neurological disorders affecting mankind. The Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans play an important role in diagnosing brain tumors, but need an expert interpretation ...
Duppala Rohan +5 more
wiley +1 more source
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 +1 more source