748 research outputs found
SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding
Remote sensing images are useful for a wide variety of planet monitoring
applications, from tracking deforestation to tackling illegal fishing. The
Earth is extremely diverse -- the amount of potential tasks in remote sensing
images is massive, and the sizes of features range from several kilometers to
just tens of centimeters. However, creating generalizable computer vision
methods is a challenge in part due to the lack of a large-scale dataset that
captures these diverse features for many tasks. In this paper, we present
SatlasPretrain, a remote sensing dataset that is large in both breadth and
scale, combining Sentinel-2 and NAIP images with 302M labels under 137
categories and seven label types. We evaluate eight baselines and a proposed
method on SatlasPretrain, and find that there is substantial room for
improvement in addressing research challenges specific to remote sensing,
including processing image time series that consist of images from very
different types of sensors, and taking advantage of long-range spatial context.
Moreover, we find that pre-training on SatlasPretrain substantially improves
performance on downstream tasks, increasing average accuracy by 18% over
ImageNet and 6% over the next best baseline. The dataset, pre-trained model
weights, and code are available at https://satlas-pretrain.allen.ai/.Comment: ICCV 202
Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection
This article investigates the potential of nonlocally filtered pursuit
monostatic TanDEM-X data for coastline detection in comparison to conventional
TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For
this task, an unsupervised coastline detection procedure based on scale-space
representations and K-medians clustering as well as morphological image
post-processing is proposed. Since this procedure exploits a clear
discriminability of "dark" and "bright" appearances of water and land surfaces,
respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data
acquired in pursuit monostatic mode is expected to provide a promising benefit.
In addition, we investigate the benefit introduced by a utilization of a
non-local InSAR filter for amplitude denoising and coherence estimation instead
of a conventional box-car filter. Experiments carried out on real TanDEM-X
pursuit monostatic data confirm our expectations and illustrate the advantage
of the employed data configuration over conventional TanDEM-X products for
automatic coastline detection
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
Multi-class vehicle detection from airborne imagery with orientation
estimation is an important task in the near and remote vision domains with
applications in traffic monitoring and disaster management. In the last decade,
we have witnessed significant progress in object detection in ground imagery,
but it is still in its infancy in airborne imagery, mostly due to the scarcity
of diverse and large-scale datasets. Despite being a useful tool for different
applications, current airborne datasets only partially reflect the challenges
of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted
vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale
dataset for multi-class vehicle detection with object orientation information
in aerial imagery. It features high-resolution aerial images composed of
different real-world situations with a wide variety of camera sensor,
resolution, flight altitude, weather, illumination, haze, shadow, time, city,
country, occlusion, and camera angle. The annotation was done by airborne
imagery experts with small- and large-vehicle classes. EAGLE contains 215,986
instances annotated with oriented bounding boxes defined by four points and
orientation, making it by far the largest dataset to date in this task. It also
supports researches on the haze and shadow removal as well as super-resolution
and in-painting applications. We define three tasks: detection by (1)
horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented
bounding boxes. We carried out several experiments to evaluate several
state-of-the-art methods in object detection on our dataset to form a baseline.
Experiments show that the EAGLE dataset accurately reflects real-world
situations and correspondingly challenging applications.Comment: Accepted in ICPR 202
DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation
Onboard intelligent processing is widely applied in emergency tasks in the
field of remote sensing. However, it is predominantly confined to an individual
platform with a limited observation range as well as susceptibility to
interference, resulting in limited accuracy. Considering the current state of
multi-platform collaborative observation, this article innovatively presents a
distributed collaborative perception network called DCP-Net. Firstly, the
proposed DCP-Net helps members to enhance perception performance by integrating
features from other platforms. Secondly, a self-mutual information match module
is proposed to identify collaboration opportunities and select suitable
partners, prioritizing critical collaborative features and reducing redundant
transmission cost. Thirdly, a related feature fusion module is designed to
address the misalignment between local and collaborative features, improving
the quality of fused features for the downstream task. We conduct extensive
experiments and visualization analyses using three semantic segmentation
datasets, including Potsdam, iSAID and DFC23. The results demonstrate that
DCP-Net outperforms the existing methods comprehensively, improving mIoU by
2.61%~16.89% at the highest collaboration efficiency, which promotes the
performance to a state-of-the-art level
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