scholarly journals Automatic Detection of Subsidence Troughs in SAR Interferograms Using Mathematical Morphology

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7785
Author(s):  
Maciej Dwornik ◽  
Stanisława Porzycka-Strzelczyk ◽  
Jacek Strzelczyk ◽  
Hubert Malik ◽  
Radosław Murdzek ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Mathematical Morphology ◽  
Detection Rate ◽  
Automatic Detection ◽  
Gabor Filters ◽  
Improved Method ◽  
Sar Images ◽  
Mining Areas ◽  
Automatic Algorithm

In this paper, an automatic algorithm for the detection of subsidence areas in SAR interferograms is proposed. It is based on the analysis of spatial distribution of the interferogram phase, and its coherence and entropy. The developed method was tested for differential interferograms generated on the basis of Sentinel-1 SAR images covering mining areas in South Poland. The obtained results were compared with those achieved using a method based on circular Gabor filters. Performed analysis revealed that the detection rate for the proposed method varied from 34% to 83%. It is an improved method based on Gabor filters that achieved a detection rate from 30% to 53%.

scholarly journals Distribution of Contaminated Soils along Transversal and Longitudinal Gradients in Dynamic Fluvial Environment (Southern Québec, Canada)

2015 ◽  
Vol 8 ◽  
pp. ASWR.S22465 ◽  
Author(s):  
Diane Saint-Laurent ◽  
Francis Baril ◽  
Ilias Bazier ◽  
Vernhar Gervais-Beaulac ◽  
Camille Chapados
Keyword(s):  
Spatial Distribution ◽  
Contaminated Soils ◽  
Soil Samples ◽  
Copper Mining ◽  
Mining Sites ◽  
Mining Areas ◽  
Pb Contamination ◽  
Flood Zone ◽  
Longitudinal Gradients ◽  
Mine Sites

This research combines a hydrological and pedological approach to better understand the spatial distribution of contaminated soils along the Massawippi River (southern Québec, Canada). This river crosses through former mines, which were some of the largest copper mining areas in North America from 1865 to 1939. To determine the spatial distribution and concentration of the metal elements, soil samples were taken in each flood recurrence zone appearing on official flood zone maps. The maximum values obtained for Cu and Pb are 380 and 200 mg kg−1, respectively, for the soils in the frequent flood zones (FFzs), while the values for soils in the moderate flood zones (MFzs) range from 700 to 540 (Cu) and 580 to 460 mg kg−1 (Pb). Contamination extends through several kilometers of the former mining sites (Eustis and Capleton), and concentration of metals in alluvial soils is slightly higher near the mine sites.

scholarly journals On Using XMC R-CNN Model for Contraband Detection within X-Ray Baggage Security Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong Zhang ◽  
Weiwu Kong ◽  
Dong Li ◽  
Xudong Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Automatic Detection ◽  
User Needs ◽  
Labor Intensity ◽  
Detection Rates ◽  
X Ray ◽  
Detection Technology ◽  
Security Inspection

We present an X-ray material classifier region-based convolutional neural network (XMC R-CNN) model for detecting the typical guns and the typical knives in X-ray baggage images. The XMC R-CNN model is used to solve the problem of contraband detection in overlapped X-ray baggage images by the X-ray material classifier algorithm and the organic stripping and inorganic stripping algorithm, and better detection rate and the miss rate are achieved. The detection rates of guns and knives are 96.5% and 95.8%, and the miss rates of guns and knives are 2.2% and 4.2%. The contraband detection technology based on the XMC R-CNN model is applied to X-ray baggage images of security inspection. According to user needs, the safe X-ray baggage images can be automatically filtered in some specific fields, which reduces the number of X-ray baggage images that security inspectors need to screen. The efficiency of security inspection is improved, and the labor intensity of security inspection is reduced. In addition, the security inspector can screen X-ray baggage images according to the boxes of automatic detection, which can improve the effect of security inspection.

scholarly journals Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

2019 ◽  
Vol 11 (7) ◽  
pp. 806 ◽  
Author(s):  
Ingri Soldal ◽  
Wolfgang Dierking ◽  
Anton Korosov ◽  
Armando Marino
Keyword(s):  
Spatial Resolution ◽  
Barents Sea ◽  
Computation Time ◽  
Automatic Detection ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Detection Scheme ◽  
Fast Ice ◽  
Wide Swath

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

A Statistical Approach for Automatic Detection of Ocean Disturbance Features From SAR Images

2012 ◽  
Vol 5 (4) ◽  
pp. 1231-1242 ◽  
Author(s):  
D. Chaudhuri ◽  
A. Samal ◽  
A. Agrawal ◽  
Sanjay ◽  
A. Mishra ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Automatic Detection ◽  
Sar Images

The Study of Counting the Strapping Steel Pipes Based on Image Recognition

2012 ◽  
Vol 490-495 ◽  
pp. 3831-3835
Author(s):  
Xue Hua Li ◽  
Hui Min Liang ◽  
Zhi Kai Cao ◽  
Zhong Yun Jiang ◽  
Long Chen
Keyword(s):  
Mathematical Morphology ◽  
Image Recognition ◽  
Small Area ◽  
Detection Rate ◽  
Hough Transformation ◽  
Steel Pipes ◽  
Economic Profit ◽  
Invariant Moments ◽  
Good Market ◽  
Inner Diameter

The accurate count has been realized and the expected results have been acquired by the image of the end of steel pipes recognized, which has a good market prospect and economic profit. The mathematical morphology was used to preprocess image and inspect the edge of the target. An improved Hough Transformation was applied to recognize inner diameter of steel pipes, which improved the detection rate. The Hu invariant moments described in the area was used to recognize inner diameter of steel pipes, which realized the pipe sum accurately. The method to count points was used to realize the sum of pipes according to the condition that the inner diameter circus of binary pipe image was corroded more times came to change into small area.

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