4,637 research outputs found
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
Source-free Domain Adaptive Object Detection in Remote Sensing Images
Recent studies have used unsupervised domain adaptive object detection
(UDAOD) methods to bridge the domain gap in remote sensing (RS) images.
However, UDAOD methods typically assume that the source domain data can be
accessed during the domain adaptation process. This setting is often
impractical in the real world due to RS data privacy and transmission
difficulty. To address this challenge, we propose a practical source-free
object detection (SFOD) setting for RS images, which aims to perform target
domain adaptation using only the source pre-trained model. We propose a new
SFOD method for RS images consisting of two parts: perturbed domain generation
and alignment. The proposed multilevel perturbation constructs the perturbed
domain in a simple yet efficient form by perturbing the domain-variant features
at the image level and feature level according to the color and style bias. The
proposed multilevel alignment calculates feature and label consistency between
the perturbed domain and the target domain across the teacher-student network,
and introduces the distillation of feature prototype to mitigate the noise of
pseudo-labels. By requiring the detector to be consistent in the perturbed
domain and the target domain, the detector is forced to focus on
domaininvariant features. Extensive results of three synthetic-to-real
experiments and three cross-sensor experiments have validated the effectiveness
of our method which does not require access to source domain RS images.
Furthermore, experiments on computer vision datasets show that our method can
be extended to other fields as well. Our code will be available at:
https://weixliu.github.io/ .Comment: 14 pages, 11 figure
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
A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification
In this paper, we present a robust and low complexity deep learning model for
Remote Sensing Image Classification (RSIC), the task of identifying the scene
of a remote sensing image. In particular, we firstly evaluate different low
complexity and benchmark deep neural networks: MobileNetV1, MobileNetV2,
NASNetMobile, and EfficientNetB0, which present the number of trainable
parameters lower than 5 Million (M). After indicating best network
architecture, we further improve the network performance by applying attention
schemes to multiple feature maps extracted from middle layers of the network.
To deal with the issue of increasing the model footprint as using attention
schemes, we apply the quantization technique to satisfies the number trainable
parameter of the model lower than 5 M. By conducting extensive experiments on
the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity
model, which is very competitive to the state-of-the-art systems and potential
for real-life applications on edge devices.Comment: 8 page
Transfer Learning for High Resolution Aerial Image Classification
With rapid developments in satellite and sensor technologies, increasing amount of high spatial resolution aerial images have become available. Classification of these images are important for many remote sensing image understanding tasks, such as image retrieval and object detection. Meanwhile, image classification in the computer vision field is revolutionized with recent popularity of the convolutional neural networks (CNN), based on which the state-of-the-art classification results are achieved. Therefore, the idea of applying the CNN for high resolution aerial image classification is straightforward. However, it is not trivial mainly because the amount of labeled images in remote sensing for training a deep neural network is limited. As a result, transfer learning techniques were adopted for this problem, where the CNN used for the classification problem is pre-trained on a larger dataset beforehand. In this paper, we propose a specific fine-tuning strategy that results in better CNN models for aerial image classification. Extensive experiments were carried out using the proposed approach with different CNN architectures. Our proposed method shows competitive results compared to the existing approaches, indicating the superiority of the proposed fine-tuning algorith
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