2 research outputs found
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
Deep learning (DL) algorithms are considered as a methodology of choice for
remote-sensing image analysis over the past few years. Due to its effective
applications, deep learning has also been introduced for automatic change
detection and achieved great success. The present study attempts to provide a
comprehensive review and a meta-analysis of the recent progress in this
subfield. Specifically, we first introduce the fundamentals of deep learning
methods which arefrequently adopted for change detection. Secondly, we present
the details of the meta-analysis conducted to examine the status of change
detection DL studies. Then, we focus on deep learning-based change detection
methodologies for remote sensing images by giving a general overview of the
existing methods. Specifically, these deep learning-based methods were
classified into three groups; fully supervised learning-based methods, fully
unsupervised learning-based methods and transfer learning-based techniques. As
a result of these investigations, promising new directions were identified for
future research. This study will contribute in several ways to our
understanding of deep learning for change detection and will provide a basis
for further research
A Survey on Object Detection in Optical Remote Sensing Images
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field.Comment: This manuscript is the accepted version for ISPRS Journal of
Photogrammetry and Remote Sensin