Cyclone track forecasting based on satellite images using artificial neural networks

Kovordanyi, Rita; Roy, Chandan

doi:10.1016/j.isprsjprs.2009.03.002

Cited by 68 publications

(31 citation statements)

References 22 publications

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“…As neural-network-based techniques can produce forecasts with acceptable accuracy, can be run on standard PCs and use widely available, inexpensive satellite images as input, they can be an excellent and inexpensive aid for forecasting cyclone track (see e.g. Kovordányi and Roy, 2009). …”

Section: Discussionmentioning

confidence: 99%

Tropical cyclone track forecasting techniques ― A review

Roy

Kovordanyi

2012

Atmospheric Research

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129

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Delivering accurate cyclone forecasts in time is of key importance when it comes to saving human lives and reducing economic loss. Difficulties arise because the geographical and climatological characteristics of the various cyclone formation basins are not similar, which entails that a single forecasting technique cannot yield reliable performance in all ocean basins. For this reason, global forecasting techniques need to be applied together with basin-specific techniques to increase the forecast accuracy. As cyclone track is governed by a range of factors variations in weather conditions, wind pressure, sea surface temperature, air temperature, ocean currents, and the earth' rotational force-the coriolis force, it is a formidable task to combine these parameters and produce reliable and accurate forecasts. In recent years, the availability of suitable data has increased and more advanced forecasting techniques have been developed, in addition to old techniques having been modified. In particular, artificial neural network based techniques are now being considered at meteorological offices. This new technique uses freely available satellite images as input, can be run on standard PCs, and can produce forecasts with good accuracy. For these reasons, artificial neural network based techniques seem especially suited for developing countries which have limited capacity to forecast cyclones and where human casualties are the highest.

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Section: Discussionmentioning

confidence: 99%

Tropical cyclone track forecasting techniques ― A review

Roy

Kovordanyi

2012

Atmospheric Research

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129

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“…Kovordanyi et al 312 utilized NNs in cyclone track forecasting. The system uses a multi-layer NN designed to mimic portions of the human visual system to analyze National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (NOAA AVHRR) imagery.…”

Section: Weather Forecastingmentioning

confidence: 99%

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Ball

Anderson

Chan

2017

J. Appl. Remote Sens

541

331

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Abstract. In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…For Input to V1, Gabor filters were used to extract oriented bar like feature for four orientations. These values for the Hebbian learning, as well as other parameters, are based on previous work [13,14].…”

Section: Mohammad Saifullah Rita Kovordányimentioning

confidence: 99%

“…Now from the figure 4, It is evident that initially Saliency_map layer build a saliency map which shows the most salient object i.e. cup (cycle: [11][12][13][14]. And Attention layer, through interaction with Salincy_map layer, activate the position of cup in Attention layer, in this case the right top unit in the Attention layer (cycle 19, 20).…”

Section: Multiple Objects With Both Bottom-up As Well As Top-down Attmentioning

confidence: 99%

Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network

Saifullah

Kovordanyi

2011

Adaptive and Natural Computing Algorithms

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Abstract:We present a computational model for visual processing where attentional focus emerges fundamental mechanisms inherent to human vision. Through detailed analysis of activation development in the network we demonstrate how normal interaction between top-down and bottom-up processing and intrinsic mutual competition within processing units can give rise to attentional focus. The model includes both spatial and object-based attention, which are computed simultaneously, and can mutually reinforce each other. We show how a non-salient location and a corresponding non-salient feature set that are at first weakly activated by visual input can be reinforced by top-down feedback signals (centrally controlled attention), and instigate a change in attentional focus to the weak object. One application of this model is highlight a task-relevant object in a cluttered visual environment, even when this object is non-salient (non-conspicuous).

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Cyclone track forecasting based on satellite images using artificial neural networks

Cited by 68 publications

References 22 publications

Tropical cyclone track forecasting techniques ― A review

Tropical cyclone track forecasting techniques ― A review

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network

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