Fine-grained object recognition in remote sensing imagery
Author
Sümbül, Gencer
Advisor
Aksoy, Selim
Date
2018-06Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
235
views
views
198
downloads
downloads
Abstract
Fine-grained object recognition aims to determine the type of an object in domains
with a large number of sub-categories. The steadily increase in spatial and
spectral resolution entailing new details in remote sensing image data, and consequently
more diversi ed target object classes having subtle di erences makes it
an emerging application. For the approaches using images from a single domain,
widespread fully supervised algorithms do not completely t into accomplishing
this problem since target object classes tend to have low between-class variance
and high within-class variance with small sample sizes. As an even more arduous
task, a method for zero-shot learning (ZSL), in which identi cation of unseen
sub-categories is tackled by associating them with previously learned seen subcategories
when there is no training example for some of the classes, is proposed.
More speci cally, our method learns a compatibility function between image representation
obtained from a deep convolutional neural network and the semantics
of target object sub-categories explained by auxiliary information gathered from
complementary sources. Knowledge transfer for unseen classes is carried out
by maximizing this function throughout the inference. Furthermore, bene tting
from multiple image sensors can overcome the drawbacks of closely intertwined
sub-categories that limits the object recognition performance. However, since
multiple images may be acquired from di erent sensors under di erent conditions
at di erent spatial and spectral resolutions, they may be geometrically unaligned
correctly due to seasonal changes, di erent viewing geometry, acquisition noise,
an imperfection of sensors, di erent atmospheric conditions etc. To address these
challenges, a neural network model that aims to correctly align images acquired
from di erent sources and to learn the classi cation rules in a uni ed framework
simultaneously is proposed. In this network, one of the sources is used as the
reference and the others are aligned with the reference image at representation level throughout a learned weighting mechanism. At the end, classi cation of
sub-categories is carried out with a feature-level fusion of representations from
the source region and estimated multiple target regions. Experimental analysis
conducted on a newly proposed data set shows that both zero-shot learning
algorithm and the multi-source ne-grained object recognition algorithm give
promising results.
Keywords
Fine-Grained Classi cationZero-Shot Learning
Multisource
Remote Sensing
Object Recognition