134,597 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
Joint Visual Denoising and Classification using Deep Learning
Visual restoration and recognition are traditionally addressed in pipeline
fashion, i.e. denoising followed by classification. Instead, observing
correlations between the two tasks, for example clearer image will lead to
better categorization and vice visa, we propose a joint framework for visual
restoration and recognition for handwritten images, inspired by advances in
deep autoencoder and multi-modality learning. Our model is a 3-pathway deep
architecture with a hidden-layer representation which is shared by multi-inputs
and outputs, and each branch can be composed of a multi-layer deep model. Thus,
visual restoration and classification can be unified using shared
representation via non-linear mapping, and model parameters can be learnt via
backpropagation. Using MNIST and USPS data corrupted with structured noise, the
proposed framework performs at least 20\% better in classification than
separate pipelines, as well as clearer recovered images. The noise model and
the reproducible source code is available at
{\url{https://github.com/ganggit/jointmodel}}.Comment: 5 pages, 7 figures, ICIP 201
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop
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