Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients
Authors
Keywords
-
Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume -, Issue -, Pages 1-32
Publisher
Informa UK Limited
Online
2019-01-10
DOI
10.1080/01431161.2018.1563841
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Semantic Segmentation of Remote Sensing Imagery Using an Object-Based Markov Random Field Model With Auxiliary Label Fields
- (2017) Chen Zheng et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
- (2016) Martin Längkvist et al. Remote Sensing
- Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov Random Field Model With Regional Penalties
- (2015) Chen Zheng et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Optimization of Segmentation Algorithms Through Mean-Shift Filtering Preprocessing
- (2013) Leiguang Wang et al. IEEE Geoscience and Remote Sensing Letters
- Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain
- (2013) Mi Chen et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Image Segmentation Using Multiregion-Resolution MRF Model
- (2012) Chen Zheng et al. IEEE Geoscience and Remote Sensing Letters
- An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery
- (2012) Xin Huang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning
- (2011) Janete S. Borges et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty
- (2011) Peter Yu et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Operational Performance of an Automatic Preliminary Spectral Rule-Based Decision-Tree Classifier of Spaceborne Very High Resolution Optical Images
- (2010) Andrea Baraldi et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs
- (2010) Sandro Martinis et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- TurboPixel Segmentation Using Eigen-Images
- (2010) Shiming Xiang et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Automatic Mapping of Linear Woody Vegetation Features in Agricultural Landscapes Using Very High Resolution Imagery
- (2009) S. Aksoy et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- TurboPixels: Fast Superpixels Using Geometric Flows
- (2009) A. Levinshtein et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Image Segmentation with a Unified Graphical Model
- (2009) Lei Zhang et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Object based image analysis for remote sensing
- (2009) T. Blaschke ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- IRGS: Image Segmentation Using Edge Penalties and Region Growing
- (2008) Qiyao Yu et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- Exploring forest structural complexity by multi-scale segmentation of VHR imagery
- (2008) A. Lamonaca et al. REMOTE SENSING OF ENVIRONMENT
- Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields
- (2007) Noel Cressie et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now