An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing

Ma, Yong; Fu, Chen; Li, Jianbo; He, Yanan; Duan, Jianbo; Li, Xinpeng

doi:10.3390/rs8040272

Cited by 26 publications

(13 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accordingly, a temporally more robust approach of bi-temporal change detection, the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) developed by A.A. Nielsen [59] is selected. The IR-MAD has been successfully used for different applications and sensors, e.g., to detect changes caused by floods from coarse AVHRR imagery [60], mining activity [61,62] and forest disturbance from Landsat data [63], as well as earthquakes from high resolution imagery of GF-1/PMS and GeoEye-1 [64,65]. The core idea of MAD is to find maximum differences between two images by removing correlations between them as much as possible [66].…”

Section: Remote Sensing Change Detection Analysesmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

View full text Add to dashboard Cite

This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas.

show abstract

Section: Remote Sensing Change Detection Analysesmentioning

confidence: 99%

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

2017

View full text Add to dashboard Cite

show abstract

“…Remote-sensing-based damage mapping is limited to the visual interpretation of optical satellite images in real-world scenarios, which is labour-intensive and time-consuming [ 7 , 8 ]. Therefore, there is an interest in automatic post-earthquake damage mapping.…”

Section: Introductionmentioning

confidence: 99%

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Matin

Pradhan

2021

Sensors

View full text Add to dashboard Cite

Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)—a machine learning model—and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model’s decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.

show abstract

“…Remote sensing has been routinely applied in emergency management such as forest fire detection and flood monitoring [34]. The fast acquisition of remote sensing data is extremely important for initiating effective response [35]. However, the satellite remote sensing imagery cannot provide timely data due to the relatively long revisit period and unpredicted weather condition.…”

Section: Introductionmentioning

confidence: 99%

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Wang

Cui

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications. To recover the cloud obscured information, some preconditions must be satisfied. For example, the cloud must be semi-transparent or relationships between contaminated and cloud-free pixels must be assumed. Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Recently, the use of passenger aircraft as a platform for remote sensing has been proposed by some researchers and institutes, including Airbus and the Japan Aerospace Exploration Agency. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud-and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate.

show abstract

An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing

Cited by 26 publications

References 59 publications

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Analyses of Recent Sediment Surface Dynamic of a Namibian Kalahari Salt Pan Based on Multitemporal Landsat and Hyperspectral Hyperion Data

Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Contact Info

Product

Resources

About