8,042 research outputs found
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.Comment: ACCV 201
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Accurately and globally mapping human infrastructure is an important and
challenging task with applications in routing, regulation compliance
monitoring, and natural disaster response management etc.. In this paper we
present progress in developing an algorithmic pipeline and distributed compute
system that automates the process of map creation using high resolution aerial
images. Unlike previous studies, most of which use datasets that are available
only in a few cities across the world, we utilizes publicly available imagery
and map data, both of which cover the contiguous United States (CONUS). We
approach the technical challenge of inaccurate and incomplete training data
adopting state-of-the-art convolutional neural network architectures such as
the U-Net and the CycleGAN to incrementally generate maps with increasingly
more accurate and more complete labels of man-made infrastructure such as roads
and houses. Since scaling the mapping task to CONUS calls for parallelization,
we then adopted an asynchronous distributed stochastic parallel gradient
descent training scheme to distribute the computational workload onto a cluster
of GPUs with nearly linear speed-up.Comment: This paper is accepted by KDD 202
Segmenting Roads from Aerial Images: A Deep Learning Approach Using Multi-Scale Analysis
Road map generation requires frequent map updates due to the irregular infrastructural changes. Updating a manual road map is a lengthy process, whereas using aerial or remote sensing (RS) requires less time for the update. However, road extraction becomes more complex due to the similar texture appearance of building top roofs, shadows, and occlusion due to trees. The occluded roads appear as discontinuous road patch in segmented image of updated maps. In this paper, we propose a deep learning method that uses multi-scale analysis for road feature extraction. The dilated inception module (DI) in the up and down sampling paths of network extracts the local and global texture patterns of the road. Furthermore, we also utilize the pyramid pooling module (PP) which has average and max pooling to study the global contextual information under the shadow regions. In the proposed architecture, first, the road in the aerial images is segmented along with the tiny non-road segments. Next, the post processing, which exploits the geometrical shape features, is utilized for filtering the tiny non-road noises. The performance of proposed network is validated on using the publicly available Massachusetts road data by comparing with the other models available in literature
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