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

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations

We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise. The method is based on a consistency loss that enables deep neural networks to be trained, given only noisy annotations as input, to correct the annotations. When some noise-free annotations are available, we show that the consistency loss reduces to a stricter self-supervised loss. We also show that the method can implicitly leverage object symmetries to reduce the ambiguity arising in correcting noisy annotations. When multiple object-annotation pairs are present in an image, we introduce a spatial memory map that allows the network to correct annotations sequentially, one at a time, while accounting for all other annotations in the image and corrections performed so far. Through ablation, we show the benefit of these contributions, demonstrating excellent results on geo-spatial imagery. Specifically, we show results using a new Railway tracks dataset as well as the public INRIA Buildings benchmarks, achieving new state-of-the-art results for the latter.Comment: BMVC 2019 (Spotlight

Development of neural units with higher-order synaptic operations and their applications to logic circuits and control problems

Neural networks play an important role in the execution of goal-oriented paradigms. They offer flexibility, adaptability and versatility, so that a variety of approaches may be used to meet a specific goal, depending upon the circumstances and the requirements of the design specifications. Development of higher-order neural units with higher-order synaptic operations will open a new window for some complex problems such as control of aerospace vehicles, pattern recognition, and image processing. The neural models described in this thesis consider the behavior of a single neuron as the basic computing unit in neural information processing operations. Each computing unit in the network is based on the concept of an idealized neuron in the central nervous system (CNS). Most recent mathematical models and their architectures for neuro-control systems have generated many theoretical and industrial interests. Recent advances in static and dynamic neural networks have created a profound impact in the field of neuro-control. Neural networks consisting of several layers of neurons, with linear synaptic operation, have been extensively used in different applications such as pattern recognition, system identification and control of complex systems such as flexible structures, and intelligent robotic systems. The conventional linear neural models are highly simplified models of the biological neuron. Using this model, many neural morphologies, usually referred to as multilayer feedforward neural networks (MFNNs), have been reported in the literature. The performance of the neurons is greatly affected when a layer of neurons are implemented for system identification, pattern recognition and control problems. Through simulation studies of the XOR logic it was concluded that the neurons with linear synaptic operation are limited to only linearly separable forms of pattern distribution. However, they perform a variety of complex mathematical operations when they are implemented in the form of a network structure. These networks suffer from various limitations such as computational efficiency and learning capabilities and moreover, these models ignore many salient features of the biological neurons such as time delays, cross and self correlations, and feedback paths which are otherwise very important in the neural activity. In this thesis an effort is made to develop new mathematical models of neurons that belong to the class of higher-order neural units (HONUs) with higher-order synaptic operations such as quadratic and cubic synaptic operations. The advantage of using this type of neural unit is associated with performance of the neurons but the performance comes at the cost of exponential increase in parameters that hinders the speed of the training process. In this context, a novel method of representation of weight parameters without sacrificing the neural performance has been introduced. A generalised representation of the higher-order synaptic operation for these neural structures was proposed. It was shown that many existing neural structures can be derived from this generalized representation of the higher-order synaptic operation. In the late 1960’s, McCulloch and Pitts modeled the stimulation-response of the primitive neuron using the threshold logic. Since then, it has become a practice to implement the logic circuits using neural structures. In this research, realization of the logic circuits such as OR, AND, and XOR were implemented using the proposed neural structures. These neural structures were also implemented as neuro-controllers for the control problems such as satellite attitude control and model reference adaptive control. A comparative study of the performance of these neural structures compared to that of the conventional linear controllers has been presented. The simulation results obtained in this research were applicable only for the simplified model presented in the simulation studies

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology

Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology