Results 111 to 120 of about 16,201 (241)

Drone‐based polarization imaging system for leaf spot severity determination in peanut plants

The Plant Phenome Journal, Volume 8, Issue 1, December 2025.
Abstract In this study, we introduce a new approach for enhancing peanut phenotyping through a polarization imaging platform. With leaf spot disease posing significant threats to peanut (Arachis hypogae L.) crops, our research addresses the need for accurate and efficient detection methods.
Joshua Larsen, Jeffrey. Dunne, Robert Austin, Cassondra Newman, Michael Kudenov   +4 more
wiley   +1 more source

Relieve the Demand for Labeled Data of Deep Learning Models for Hydraulic Conductivity Field Tasks in Groundwater Through Self‐Supervised Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 4, December 2025.
Abstract Deep learning (DL) has shown great potential in solving groundwater problems but often requires large labeled data sets, which are expensive and time‐consuming to obtain. In this study, we introduce a self‐supervised learning approach based on a masked autoencoder (MAE)—an encoder‐decoder architecture that reconstructs randomly masked input ...
Kai Ji, Zhengkun Zhou, Bo Zou, Ruiting Yang, Baoqing Zhang   +4 more
wiley   +1 more source

Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels

Journal of Pathology Informatics, 2018
Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images.
Sudhir Sornapudi, R. Stanley, W. Stoecker, Haidar A. Almubarak, Rodney Long, Sameer Kiran Antani, G. Thoma, R. Zuna, S. R. Frazier   +8 more
semanticscholar   +1 more source

A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

Remote Sensing, 2019
Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks.
Maria Papadomanolaki, Maria Vakalopoulou, Konstantinos Karantzalos   +2 more
doaj   +1 more source

A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

PLoS ONE, 2017
The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the ...
Wei Zhang, Xiaolong Zhang, Juanjuan Zhao, Yan Qiang, Qi Tian, Xiaoxian Tang   +5 more
semanticscholar   +1 more source

The Outlining of Agricultural Plots Based on Spatiotemporal Consensus Segmentation

Remote Sensing, 2018
The outlining of agricultural land is an important task for obtaining primary information used to create agricultural policies, estimate subsidies and agricultural insurance, and update agricultural geographical databases, among others.
Angel Garcia-Pedrero, Consuelo Gonzalo-Martín, Mario Lillo-Saavedra, Dionisio Rodríguez-Esparragón   +3 more
doaj   +1 more source

Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation

Remote Sensing, 2018
Image over-segmentation aims to partition an image into spatially adjacent and spectrally homogeneous regions. It could reduce the complexity of image representation and enhance the efficiency of subsequent image processing.
Hong Tang, Xuejun Zhai, Wei Huang
doaj   +1 more source

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Remote Sensing, 2016
Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a ...
Qiao Xu, Qihao Chen, Shuai Yang, Xiuguo Liu   +3 more
doaj   +1 more source

Adaptive Improved GCNs and SAM Superpixels for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image (HSI) classification with limited training samples is a challenging problem. According to recent results, effectively exploiting the spatial–spectral information of the HSI is crucial for HSI classification, even when the ...
Lei Wang, Wen-Sheng Zhu, Shi-Wen Deng
doaj   +1 more source

Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

Remote Sensing, 2019
The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral−Spatial ...
Aizhu Zhang, Shuang Zhang, Genyun Sun, Feng Li, Hang Fu, Yunhua Zhao, Hui Huang, Ji Cheng, Zhenjie Wang   +8 more
doaj   +1 more source