2011
DOI: 10.14358/pers.77.10.1025
|View full text |Cite
|
Sign up to set email alerts
|

Damage Assessment of 2010 Haiti Earthquake with Post-Earthquake Satellite Image by Support Vector Selection and Adaptation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
12
0

Year Published

2012
2012
2024
2024

Publication Types

Select...
6
1
1

Relationship

0
8

Authors

Journals

citations
Cited by 42 publications
(12 citation statements)
references
References 16 publications
0
12
0
Order By: Relevance
“…For a given PolSAR image L, M denotes the rows of the image, N denotes the columns of the image, i indicates the row number, j indicates the column number, x(i, j) represents an arbitrary pixel, K x(i,j) corresponds to the Kennaugh matrix of x(i, j). MaxC x_CB denotes the maximal power contrast between pixel x(i, j) and the target template, as shown in Equation ( 7), and MaxC CB_x denotes the maximal power contrast between the target template and pixel x(i, j), as shown in Equation (8). MaxC represents the final maximal power contrast which is also the output result of the OPCE matching algorithm.…”
Section: Inputmentioning
confidence: 99%
See 1 more Smart Citation
“…For a given PolSAR image L, M denotes the rows of the image, N denotes the columns of the image, i indicates the row number, j indicates the column number, x(i, j) represents an arbitrary pixel, K x(i,j) corresponds to the Kennaugh matrix of x(i, j). MaxC x_CB denotes the maximal power contrast between pixel x(i, j) and the target template, as shown in Equation ( 7), and MaxC CB_x denotes the maximal power contrast between the target template and pixel x(i, j), as shown in Equation (8). MaxC represents the final maximal power contrast which is also the output result of the OPCE matching algorithm.…”
Section: Inputmentioning
confidence: 99%
“…Various optical-based studies for building damage detection have been proposed. The related studies vary from the methods based on multi-temporal optical images [7] to the methods based on single-temporal optical image [8], from the methods based on a single optical platform to the methods based on multiple optical platforms [9], from the methods based on pixels [7] to the methods based on objects [8], from the methods using machine learning [10] to the methods utilizing deep learning [11]. Optical-based methods have been studied widely and can obtain accurate detection results of building damage.…”
Section: Introductionmentioning
confidence: 99%
“…Texture and structure features were derived from pre-and post-earthquake VHR satellite imagery for the city of Port-au-Prince (Haiti), and obtained overall accuracies of 74.1-77.3% and Kappa values of 30.6-40.2% using artificial neural networks (ANN), radial basis function neural network (RBFNN) and RF [1]. A support vector selection and adaptation (SVSA) approach was carried out, to classify the post-earthquake QuickBird data into eight land-use classes, and 92 damaged buildings were correctly identified from the total 145 damaged samples [31]. The road and building classes were confused with the damage class due to pixel-based classification.…”
Section: Cnns For Identifying Earthquake-induced Collapsed Buildingsmentioning
confidence: 99%
“…The collapsed buildings were detected by methods based on object-based image analysis (OBIA), and SVM using post-event LiDAR data [30]. A support vector selection and adaptation (SVSA) method was applied to two small regions and the entire city of Port-au-Prince (Haiti), to assess the damage using the post-event satellite images [31]. A variety of algorithms and parameters were tested on post-event aerial imagery for the earthquake in Christchurch, New Zealand, and the results showed that object-based approaches can produce better results than pixel-based approaches in earthquake damage detection using remotely sensed images [32].…”
Section: Introductionmentioning
confidence: 99%
“…Two major innovations have improved our ability to rapidly assess damage in the aftermath of a major earthquake event: high spatial resolution remote sensing imagery and online crowdsourcing [1,2]. In the two days after the 2011 earthquake in New Zealand, nearly 70,000 unique online visitors amassed 779 reports that informed the activities of local volunteers who helped clear more than 360,000 tons of silt and rubble [3].…”
Section: Introductionmentioning
confidence: 99%