1,285 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
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
Semantic segmentation is the pixel-wise labelling of an image. Since the
problem is defined at the pixel level, determining image class labels only is
not acceptable, but localising them at the original image pixel resolution is
necessary. Boosted by the extraordinary ability of convolutional neural
networks (CNN) in creating semantic, high level and hierarchical image
features; excessive numbers of deep learning-based 2D semantic segmentation
approaches have been proposed within the last decade. In this survey, we mainly
focus on the recent scientific developments in semantic segmentation,
specifically on deep learning-based methods using 2D images. We started with an
analysis of the public image sets and leaderboards for 2D semantic
segmantation, with an overview of the techniques employed in performance
evaluation. In examining the evolution of the field, we chronologically
categorised the approaches into three main periods, namely pre-and early deep
learning era, the fully convolutional era, and the post-FCN era. We technically
analysed the solutions put forward in terms of solving the fundamental problems
of the field, such as fine-grained localisation and scale invariance. Before
drawing our conclusions, we present a table of methods from all mentioned eras,
with a brief summary of each approach that explains their contribution to the
field. We conclude the survey by discussing the current challenges of the field
and to what extent they have been solved.Comment: Updated with new studie
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved
state-of-the-art results in image Super-Resolution (SR), representing an
exceptional opportunity for the remote sensing field to extract further
information and knowledge from captured data. However, most of the works
published in the literature have been focusing on the Single-Image
Super-Resolution problem so far. At present, satellite based remote sensing
platforms offer huge data availability with high temporal resolution and low
spatial resolution. In this context, the presented research proposes a novel
residual attention model (RAMS) that efficiently tackles the multi-image
super-resolution task, simultaneously exploiting spatial and temporal
correlations to combine multiple images. We introduce the mechanism of visual
feature attention with 3D convolutions in order to obtain an aware data fusion
and information extraction of the multiple low-resolution images, transcending
limitations of the local region of convolutional operations. Moreover, having
multiple inputs with the same scene, our representation learning network makes
extensive use of nestled residual connections to let flow redundant
low-frequency signals and focus the computation on more important
high-frequency components. Extensive experimentation and evaluations against
other available solutions, either for single or multi-image super-resolution,
have demonstrated that the proposed deep learning-based solution can be
considered state-of-the-art for Multi-Image Super-Resolution for remote sensing
applications
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
A Comprehensive Literature Review on Convolutional Neural Networks
The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further
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