One-stage oriented object detection in remote sensing images
Author(s)
Advisor
Aksoy, SelimDate
2022-03Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
Advances in technology resulted in an enormous amount of information collected
from high technology satellites and aircraft sensors. These high-resolution
images obtained by the said platforms enabled humans to understand the Earth
better. The first studies in the field of remote sensing image analysis have focused
on image classification, which studies the understanding of scenes, terrain,
and vast fields. Recently object detection and image segmentation on very highresolution
electro-optical images have become popular.
Modern object detectors have two types of architecture. Two-stage object
detectors screen probable object locations with a region proposal mechanism and
regress over these preselected regions to classify objects. On the other hand, onestage
object detectors perform global regression and classification over the image
without any preselection. The region proposal mechanism improves the accuracy
but slows the detection speed, making one-stage object detectors better for realtime
detection, which have lower accuracy. Publicly available data sets generally
provide horizontal bounding box labels. However, remote sensing images are
dominated by small, congested, or large aspect ratio objects, which is suitable for
oriented bounding boxes.
We study one-stage object detection in one of the most extensive remote sensing
object detection data sets (DOTA), which includes fifteen object classes. Our
one-stage object detectors are based on one of the most prominent one-stage object
detector architectures (YOLO). Horizontal and oriented bounding box labels
are used to obtain results to show that the oriented object detection setting has
a surpassing performance over axis-aligned object detection setting in general
and also to show that one-stage object detection can have competitive results
compared to two-stage object detection methods. Our oriented object detection
approach converts angle regression into a form of classification and is easily applicable
to the one-stage detectors. Our experiments show that rotated object
detection based on angular classification performs better than horizontal object
detector. It also shows 3.07% improvement over published two-stage methods
and 12.84% improvement over published anchor-free methods on the validation
data set.