PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
March 2017
167
PHOTOGRAMME TR I C ENG I NE ER I NG & REMOT E SENS I NG
The official journal for imaging and geospatial information science and technology
March 2017 Volume 83 Number 3
onathan Rupprecht
ow does information influence me and my business? How does the information about the new
federal drone regulations, and state and local law issues influence you and your business? With the great influx of
pilots in this area, is your service going to
become more and more commoditized? Are there particular waivers that you need now to fully realize the cost-saving
potential of your use of drones? Should you start talking with
your local elected officials to prevent any potential local drone laws? What is your game plan on staying up to date on
changes? Keep these questions in mind as we dive into each of these points.
FEATURES
PEER-REVIEWED ARTICLES
COLUMNS
The Column of the Student Advisory Council.
This month we look at the The Kingdom of Morocco.
ANNOUNCEMENTS
International Lidar Mapping Forum (ILMF) and ASPRS
Annual Conference to Tak e Place Together in Denver in
2018; ASPRS seeks new editors.
Join us in welcoming our newest members to ASPRS.
Remote Sensing of Urban Environment
DEPARTMENTS
Stand Out From the Rest! Newly certified and re-certified
professionals.
Bharath Bhushan Damodaran, Joachim Höhle,
and
Sébastien Lefèvre
A new attribute generation technique for very high resolution imagery produces accurate
land cover maps of urban areas.
Xiaobing Han, Yanfei Zhong,
and
Liangpei Zhang
Efficiently exploiting the combination of the finer spatial and spectral information from
hyperspectral imagery to further improve the classification performance of the ground
features.
Mehdi Saati
and
Jalal Amini
A study to develop a method to extract road networks from high-resolution SAR imagery.
Jin Wang, Zhenqi Hu, Yanyan Chen,
and
Zhiqing Zhang
Automatic estimation of road slopes and superelevations using mobile laser scanning
through segmentation, dynamic blocks, and denoising.
Jinxia Zhu, Yanjun Su, Qinghua Guo,
and
Thomas C. Harmon
An automated method based on the object-based multivariate alteration detection/
maximum autocorrelation factor approach and the Gaussian mixture model-expectation
maximization algorithm to obtain unsupervised difference images.
Amina Rangoonwala, Cathleen E. Jones, Zhaohui Chi,
and
Elijah Ramsey III
Improving the response and flexibility and advancing the automation of shoreline
mapping.
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