scholarly journals On the Use of Single-, Dual-, and Quad-Polarimetric SAR Observation for Landslide Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 384 ◽  
Author(s):  
Park ◽  
Lee
Keyword(s):  
Remote Sensing ◽  
Detection Rate ◽  
Detection Method ◽  
Incidence Angle ◽  
Accuracy Assessment ◽  
Temporal Variations ◽  
The Other ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Simple Change

Remote sensing technologies, particularly with Synthetic Aperture Radar (SAR) system, can provide timely and critical information to assess landslide distributions over large areas. Most space-borne SAR systems have been operating in different polarimetric modes to meet various operational requirements. This study aims to discuss how much detectability can be expected in the landslide map produced from the single-, dual-, and quad-polarization modes of observation. The experimental analysis of the characteristic changes of PALSAR-2 signals showed that quad-polarization parameters indicating signal depolarization properties revealed noticeable landslide-induced temporal changes for all local incidence angle ranges. To produce a landslide map, a simple change detection method based on characteristic scattering properties of landslide areas was proposed. The accuracy assessment results showed that the depolarization parameters, such as the co-pol coherence and polarizing contribution, can identify areas affected by landslides with a detection rate of 60%, and a false-alarm rate of 5%. On the other hand, the single- or dual-pol parameters can only be expected to provide half the accuracy with significant false-alarms in areas with temporal variations independent of landslides.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals Monitoring and Detecting Archaeological Features with Multi-Frequency Polarimetric Analysis

2019 ◽  
Vol 12 (1) ◽  
pp. 1 ◽  
Author(s):  
Jolanda Patruno ◽  
Magdalena Fitrzyk ◽  
Jose Manuel Delgado Blasco
Keyword(s):  
Remote Sensing ◽  
Cultural Heritage ◽  
Synthetic Aperture Radar ◽  
Incidence Angle ◽  
Small Dimension ◽  
Synthetic Aperture ◽  
Alos Palsar ◽  
Multi Temporal ◽  
L Band ◽  
Aperture Radar

In remote sensing for archaeology, an unequivocal method capable of automatic detection of archaeological features still does not exists. Applications of Synthetic Aperture Radar (SAR) remote sensing for archaeology mainly focus on high spatial resolution SAR sensors, which allow the recognition of structures of small dimension and give information of the surface topography of sites. In this study we investigated the potential of combined dual and fully polarized SAR data and performed polarimetric multi-frequency and multi-incidence angle analysis of C-band Sentinel-1, L-band Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) and of C-band Radar Satellite-2 (RADARSAT-2) datasets for the detection of surface and subsurface archaeological structures over the United Nations Educational, Scientific and Cultural Organization (UNESCO) site of Gebel Barkal (Sudan). While PALSAR offers a good historical reference, Sentinel-1 time series provide recent and systematic monitoring opportunities. RADARSAT-2 polarimetric data have been specifically acquired in 2012/2013, and have been scheduled to achieve a multi-temporal observation of the archaeological area under study. This work demonstrated how to exploit a complex but significant dataset composed of SAR full polarimetric and dual polarimetric acquisitions, with the purpose of identifying the most suitable earth observation technique for the preservation and identification of archaeological features. The scientific potential of the illustrated analysis fits perfectly with the current delicate needs of cultural heritage; such analysis demonstrates how multi-temporal and multi-data cultural heritage monitoring can be applied not only for documentation purposes, but can be addressed especially to those areas exposed to threats of different nature that require a constant and prompt intervention plans.

scholarly journals A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

2019 ◽  
Vol 11 (6) ◽  
pp. 620 ◽  
Author(s):  
Jun Wang ◽  
Tong Zheng ◽  
Peng Lei ◽  
Xiao Bai
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Regions Of Interest ◽  
The Other ◽  
Synthetic Aperture ◽  
Constant False Alarm Rate ◽  
Sar Imaging ◽  
Detection Stage ◽  
Sar Imagery

The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.

An Adaptive Vigilance Task with Knowledge of Results

1974 ◽  
Vol 16 (4) ◽  
pp. 333-338 ◽  
Author(s):  
Earl L. Wiener
Keyword(s):  
Detection Rate ◽  
Adaptive Strategies ◽  
Vigilance Task ◽  
Performance Measure ◽  
The Other ◽  
False Alarms ◽  
Past Performance ◽  
Knowledge Of Results ◽  
Computer Controlled ◽  
Traditional Performance

Four groups of subjects performed a 48-min, computer-controlled, visual watch-keeping task. Two groups were run under fixed, non-adaptive conditions, one with immediate knowledge of results (KR) and the other without (NKR). The KR group showed the usual superiority in detection rate over the NKR group, and made fewer commissive errors (false alarms). Two other groups, also KR and NKR, ran under adaptive conditions, wherein the size of the signals they watched for was adjusted during the vigil according to past performance, so as to maintain a preset detection rate. The resulting curves for the adaptive variable closely resembled the traditional performance measure, detection rate. Various adaptive strategies are discussed.

scholarly journals Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1250 ◽  
Author(s):  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
Marie Ballère ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Time Series ◽  
Detection Rate ◽  
Large Scale ◽  
Detection Method ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Shadowing Effect ◽  
Potential Exploitation ◽  
Better Than

To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

Development and Validation of a Cultural Method for the Detection and Isolation of Salmonella in Cloves

2017 ◽  
Vol 80 (3) ◽  
pp. 376-382 ◽  
Author(s):  
Guodong Zhang ◽  
Laila Ali ◽  
Vikas Gill ◽  
Aparna Tatavarthy ◽  
Xiaohong Deng ◽  
...  
Keyword(s):  
Drug Administration ◽  
Detection Rate ◽  
Detection Method ◽  
Food And Drug Administration ◽  
Antimicrobial Properties ◽  
The Other ◽  
Test Portion ◽  
Portion Sizes ◽  
Development And Validation ◽  
The U.S

ABSTRACT Detection of Salmonella in some spices, such as cloves, remains a challenge due to their inherent antimicrobial properties. The purpose of this study was to develop an effective detection method for Salmonella from spices using cloves as a model. Two clove varieties, Ceylon and Madagascar, were used in the study. Cloves were inoculated with Salmonella enterica subsp. enterica serotypes Montevideo, Typhimurium, or Weltevreden at about 1, 3, or 6 log CFU/25 g. Two test portion sizes, 10 and 25 g, were compared. After adding Trypticase soy broth (TSB) to the weighed cloves for preenrichment, three preenrichment methods were compared: cloves were left in the TSB for 24 h during preenrichment (PreE1), or the cloves-TSB mixture was shaken vigorously for 30 s (PreE2) or 60 s (PreE3), and the decanted material was transferred to a new bag for 24 h of preenrichment. The rest of the procedures were carried out according to the U.S. Food and Drug Administration Bacteriological Analytical Manual (BAM). At the low inoculation level (<1 log CFU/25 g), the detection rate was low across the three preenrichment methods, with the highest for PreE3 and lowest for PreE1. At the medium and high inoculation levels (3 and 6 log CFU/25 g), all samples from PreE2 and PreE3 were positive for Salmonella, whereas PreE1 produced only 12 positive samples from the 48 samples at the medium inoculation level and 38 positive samples from the 48 samples at the high inoculation level. Therefore, PreE3 with 25 g of cloves per sample was more effective than the other two tested methods. This newly designed method was then validated by comparing with the BAM method in six trials, with each trial consisting of 40 test samples. The results showed that PreE3 detected Salmonella from 88 of 120 inoculated test samples compared with only 31 positive from 120 test samples with the BAM method. Thus, our newly designed method PreE3 was more sensitive and easier to operate than the current BAM method for detection of Salmonella in cloves.

scholarly journals MARITIME PRODUCTS USING TERRASAR-X and SENTINEL-1 IMAGERY

2015 ◽  
Vol XL-7/W3 ◽  
pp. 967-973 ◽  
Author(s):  
S. Lehner ◽  
B. Tings
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Wind Field ◽  
Oil Spills ◽  
Incidence Angle ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Sea State ◽  
Radar Backscatter ◽  
Sea Surface Roughness

High resolution remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X/Tandem-X satellites are used to determine and monitor the sea surface in near real time and all weather and illumination conditions. The radar backscatter of the sea surface is determined by the sea surface roughness caused by the wind field and the sea state. These meteo parameters are modelled by the newly developed algorithms XMOD and XWAVE relating the wind field and sea state, depending on incidence angle and directionality to the radar backscatter sigma0. <br><br> The TerraSAR-X Modes Stripmap, Scan SAR and Scan SAR Wide are used together with Sentinel and RADARSAT data to detect ships, oil spills and icebergs. The detectability depending on the background conditions is discussed. Several examples from near real time campaigns performed together with users are given.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document