PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
September 2020
523
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
September 2020 Volume 86 Number 9
524
Emre Ba eski
Automatic image exploitation is a critical technology for quick content analysis of high-resolution
remote sensing images. The presence of a heliport on an image usually implies an important
facility, such as military facilities. Therefore, detection of heliports can reveal critical information
about the content of an image. In this article, two learning-based algorithms are presented that
make use of artificial neural networks to detect H-shaped, light-colored heliports.
Hasan Tonbul
and
Taskin Kavzoglu
Due to the complex spectral and spatial structures of remotely sensed images, the delineation
of land use/land cover classes using conventional approaches is a challenging task. This article
tackles the problem of seeking optimal parameters of multi-resolution segmentation for a
classification task using WorldView-2 imagery.
Ali Ozgun Ok
and
Asli Ozdarici-Ok
In this study, we present an original unified strategy for the precise extraction of individual citrus
fruit trees from single digital surface model (DSM) input data. A probabilistic method combining the
circular shape information with the knowledge of the local maxima in the DSM has been used for
the detection of the candidate trees.
Ismail Colkesen
and
Omer Habib Ertekin
In this study, the performances of random forest (RF), rotation forest (RoF), and canonical correlation
forest (CCF) algorithms were compared and analyzed for classification of hyperspectral imagery.
Mehmet Akif Günen, Umit Haluk Atasever,
and
Erkan Be dok
Autoencoder (AE)-based deep neural networks learn complex problems by generating feature-space
conjugates of input data. The learning success of an AE is too sensitive for a training algorithm.
The problem of hyperspectral image (HSI) classification by using spectral features of pixels is a
highly complex problem due to its multi-dimensional and excessive data nature. In this paper, the
contribution of three gradient-based training algorithms (i.e., scaled con-jugate gradient (SCG),
gradient descent (GD), and resilient backpropagation algorithms (RP)) on the solution of the HSI
classification problem by using AE was analyzed
linkedin.com/groups/2745128/profile
By Al Karlin, Ph.D, CMS-L, GISP
FEATURE
COLUMNS
This month we look at the Republic of Kiribati
The Column of the Student Advisory Council
ANNOUNCEMENTS
Join us in welcoming our newest members to ASPRS.
DEPARTMENTS
By Raechel Portelli, Colin Brooks,
Nancy French, Kathleen Bergen,
Laura Bourgeau-Chavez,
and Marguerite Madden
