701 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images
With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of remotely sensed images. However, the exploration of these images still involves a tremendous amount of human interventions, which are tedious, time-consuming, and inefficient. To help imaging experts gain a complete understanding of the images and locate the objects of interest in a more accurate and efficient way, there is always an urgent need for developing automatic detection algorithms. In this work, we delve into the object detection problems in remote sensing applications, exploring the detection algorithms for both hyperspectral images (HSIs) and high resolution aerial images.
In the first part, we focus on the subpixel target detection problem in HSIs with low spatial resolutions, where the objects of interest are much smaller than the image pixel spatial resolution. To this end, we explore the detection frameworks that integrate image segmentation techniques in designing the matched filters (MFs). In particular, we propose a novel image segmentation algorithm to identify the spatial-spectral coherent image regions, from which the background statistics were estimated for deriving the MFs. Extensive experimental studies were carried out to demonstrate the advantages of the proposed subpixel target detection framework. Our studies show the superiority of the approach when comparing to state-of-the-art methods.
The second part of the thesis explores the object based image analysis (OBIA) framework for geospatial object detection in high resolution aerial images. Specifically, we generate a tree representation of the aerial images from the output of hierarchical image segmentation algorithms and reformulate the object detection problem into a tree matching task. We then proposed two tree-matching algorithms for the object detection framework. We demonstrate the efficiency and effectiveness of the proposed tree-matching based object detection framework.
In the third part, we study object detection in high resolution aerial images from a machine learning perspective. We investigate both traditional machine learning based framework and end-to-end convolutional neural network (CNN) based approach for various object detection tasks. In the traditional detection framework, we propose to apply the Gaussian process classifier (GPC) to train an object detector and demonstrate the advantages of the probabilistic classification algorithm. In the CNN based approach, we proposed a novel scale transfer module that generates enhanced feature maps for object detection. Our results show the efficiency and competitiveness of the proposed algorithms when compared to state-of-the-art counterparts
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
Geospatial object detection of remote sensing imagery has been attracting an
increasing interest in recent years, due to the rapid development in spaceborne
imaging. Most of previously proposed object detectors are very sensitive to
object deformations, such as scaling and rotation. To this end, we propose a
novel and efficient framework for geospatial object detection in this letter,
called Fourier-based rotation-invariant feature boosting (FRIFB). A
Fourier-based rotation-invariant feature is first generated in polar
coordinate. Then, the extracted features can be further structurally refined
using aggregate channel features. This leads to a faster feature computation
and more robust feature representation, which is good fitting for the coming
boosting learning. Finally, in the test phase, we achieve a fast pyramid
feature extraction by estimating a scale factor instead of directly collecting
all features from image pyramid. Extensive experiments are conducted on two
subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness
of the FRIFB compared to previous state-of-the-art methods
Few-shot Object Detection on Remote Sensing Images
In this paper, we deal with the problem of object detection on remote sensing
images. Previous methods have developed numerous deep CNN-based methods for
object detection on remote sensing images and the report remarkable
achievements in detection performance and efficiency. However, current
CNN-based methods mostly require a large number of annotated samples to train
deep neural networks and tend to have limited generalization abilities for
unseen object categories. In this paper, we introduce a few-shot learning-based
method for object detection on remote sensing images where only a few annotated
samples are provided for the unseen object categories. More specifically, our
model contains three main components: a meta feature extractor that learns to
extract feature representations from input images, a reweighting module that
learn to adaptively assign different weights for each feature representation
from the support images, and a bounding box prediction module that carries out
object detection on the reweighted feature maps. We build our few-shot object
detection model upon YOLOv3 architecture and develop a multi-scale object
detection framework. Experiments on two benchmark datasets demonstrate that
with only a few annotated samples our model can still achieve a satisfying
detection performance on remote sensing images and the performance of our model
is significantly better than the well-established baseline models.Comment: 12pages, 7 figure
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Remote sensing object detection (RSOD), one of the most fundamental and
challenging tasks in the remote sensing field, has received longstanding
attention. In recent years, deep learning techniques have demonstrated robust
feature representation capabilities and led to a big leap in the development of
RSOD techniques. In this era of rapid technical evolution, this review aims to
present a comprehensive review of the recent achievements in deep learning
based RSOD methods. More than 300 papers are covered in this review. We
identify five main challenges in RSOD, including multi-scale object detection,
rotated object detection, weak object detection, tiny object detection, and
object detection with limited supervision, and systematically review the
corresponding methods developed in a hierarchical division manner. We also
review the widely used benchmark datasets and evaluation metrics within the
field of RSOD, as well as the application scenarios for RSOD. Future research
directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than
300 papers relevant to the RSOD filed were reviewed in this surve
Satellite Imagery Multiscale Rapid Detection with Windowed Networks
Detecting small objects over large areas remains a significant challenge in
satellite imagery analytics. Among the challenges is the sheer number of pixels
and geographical extent per image: a single DigitalGlobe satellite image
encompasses over 64 km2 and over 250 million pixels. Another challenge is that
objects of interest are often minuscule (~pixels in extent even for the highest
resolution imagery), which complicates traditional computer vision techniques.
To address these issues, we propose a pipeline (SIMRDWN) that evaluates
satellite images of arbitrarily large size at native resolution at a rate of >
0.2 km2/s. Building upon the tensorflow object detection API paper, this
pipeline offers a unified approach to multiple object detection frameworks that
can run inference on images of arbitrary size. The SIMRDWN pipeline includes a
modified version of YOLO (known as YOLT), along with the models of the
tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed
approach allows comparison of the performance of these four frameworks, and can
rapidly detect objects of vastly different scales with relatively little
training data over multiple sensors. For objects of very different scales (e.g.
airplanes versus airports) we find that using two different detectors at
different scales is very effective with negligible runtime cost.We evaluate
large test images at native resolution and find mAP scores of 0.2 to 0.8 for
vehicle localization, with the YOLT architecture achieving both the highest mAP
and fastest inference speed.Comment: 8 pages, 7 figures, 2 tables, 1 appendix. arXiv admin note:
substantial text overlap with arXiv:1805.0951
- …