A survey of disaster management and SAR operations using sensors and supporting techniques

AlAli, Zahraa Tarik; Alabady, Salah Abdulghani

doi:10.1016/j.ijdrr.2022.103295

Cited by 23 publications

(4 citation statements)

References 66 publications

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“…Synthetic Aperture Radar sensors are especially famous for their improved spatial resolution capability [143]. In our experiments, two different resolution‐based datasets have been used.…”

Section: Resultsmentioning

confidence: 99%

“…[142] as the input of the proposed model and using the EL strategies in our SSELF, not only the classification performance of the proposed model can remain high in the case of small amounts of variance, but it can also remain constant by adding a large amount of the variance of the Gaussian White noise. 4.5.6 | The effect of the spatial resolution of the SAR data on the PolSAR image classification task Synthetic Aperture Radar sensors are especially famous for their improved spatial resolution capability [143]. In our experiments, two different resolution-based datasets have been used.…”

Section: Examining the Robustness Of The Proposed Methods Against Noi...mentioning

confidence: 99%

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A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Darvishnezhad,

Sebt

2023

IET Radar Sonar & Navi

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Classification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

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Section: Resultsmentioning

confidence: 99%

Section: Examining the Robustness Of The Proposed Methods Against Noi...mentioning

confidence: 99%

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Darvishnezhad,

Sebt

2023

IET Radar Sonar & Navi

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“…This information can be combined with ground seismic sensors to make a more accurate early warning of earthquakes [71]. Infrared satellites can predict earthquakes by detecting anomalous ground warming prior to an earthquake [72] . The satellite information can be used by different agencies in different levels.…”

Section: Satellitementioning

confidence: 99%

Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors

Tian¹,

Liu²,

Mo³

et al. 2023

Preprint

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The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, physical or chemical mechanisms of sensors, and location of sensor platforms. In this study, we analyzed all available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks.

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“…SAR performs pulse compression on the returned echo signal and then utilizes imaging techniques to generate high-precision images of the target [3]. Spaceborne SAR provides extremely high altitude and thus enables observation over a wide area [4], widely applied in environmental monitoring [5], disaster warning [6], and geographic inversion [7]. However, the growing number of electromagnetic devices in the universe space, ground, and ocean leads to the overlapping utilization of the same spectrum, therefore the electromagnetic environment in which the Spaceborne SAR is located becomes increasingly harsh [8].…”

Section: Introductionmentioning

confidence: 99%

MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

Ran

Zhang

et al. 2022

Remote Sensing

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Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.

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A survey of disaster management and SAR operations using sensors and supporting techniques

Cited by 23 publications

References 66 publications

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors

MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

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