M. Chapman

Publications80
h-index20
Citations1,954
Highly Influential Citations128
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Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
TLDR
An end-to-end spectral–spatial residual network that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification and achieves the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets.
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Using mobile laser scanning data for automated extraction of road markings
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An integrated INS/GPS approach to the georeferencing of remotely sensed data
A general model for the georeferencing of remotely sensed data by an onboard positioning and orientation system is presented as a problem of rigid body motion. The determination of the six…
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
TLDR
This article provides a systematic review of existing compelling DL architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving, such as segmentation, detection, and classification.
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Automated Road Information Extraction From Mobile Laser Scanning Data
TLDR
This paper describes the development of automated algorithms for extracting road features (road surfaces, road markings, and pavement cracks) from MLS point cloud data and concludes that MLS is a reliable and cost-effective alternative for rapid road inspection.
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Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review
TLDR
The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.
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Segmentation of SAR Intensity Imagery With a Voronoi Tessellation, Bayesian Inference, and Reversible Jump MCMC Algorithm
This paper presents a region-based approach to segmentation of the satellite synthetic aperture radar (SAR) intensity imagery. The approach is based on a Voronoi tessellation, the Bayesian inference,…
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Multi-Scale Point-Wise Convolutional Neural Networks for 3D Object Segmentation From LiDAR Point Clouds in Large-Scale Environments
TLDR
The proposed end-to-end feature extraction framework for 3D point cloud segmentation by using dynamic point-wise convolutional operations in multiple scales can achieve state-of-the-art semantic segmentation performance in feature representativeness, segmentation accuracy, and technical robustness.
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Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice in Labrador Coast
TLDR
This study aims at proposing a semiautomated sea ice segmentation workflow utilizing Sentinel-1 synthetic aperture radar imagery, which was tested on open water segmentation per ice charts provided by Canada Ice Service.
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Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests
TLDR
Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas, so current advances in object-based image analysis and machine learning algorithms are taken to reduce manual image interpretation and automate feature selection in a classification process.
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