Levee Slide Detection using Synthetic Aperture Radar Magnitude and Phase

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers

Accuracy Analysis Comparison of Supervised Classification Methods for Anomaly Detection on Levees Using SAR Imagery

This paper analyzes the use of a synthetic aperture radar (SAR) imagery to support levee condition assessment by detecting potential slide areas in an efficient and cost-effective manner. Levees are prone to a failure in the form of internal erosion within the earthen structure and landslides (also called slough or slump slides). If not repaired, slough slides may lead to levee failures. In this paper, we compare the accuracy of the supervised classification methods minimum distance (MD) using Euclidean and Mahalanobis distance, support vector machine (SVM), and maximum likelihood (ML), using SAR technology to detect slough slides on earthen levees. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band SAR imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) uninhabited aerial vehicle synthetic aperture radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the Southern USA, where earthen flood control levees are maintained by the US Army Corps of Engineers

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small

Runway Detection Using Classification Based on Polarimetric Decomposed Features

© 2017 IEEE. Current developments in the arena of remote sensing technology has opened innovative prospects and impelled the way of analyzing images from remote sensing satellites to detect or identify an object, or a place, which is selected as an area of interest. The detection of airport becomes a motivating topic recently because of its applications and importance in military and civil aviation fields. This paper presents an approach for airport detection using remote sensing images by implementing unsupervised classification techniques based on polarimetric decomposed features that include Entropy, Eigenvalue parameters, and Alpha angle. The obtained results reveal that classification based on decomposed polarimetric features provides better results for the detection of the target. The classification based Eigenvalue parameter 2 gives superior results compared to Eigenvalue parameter 1 and 3. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band Polarimetric Synthetic Aperture Radar (poISAR) imagery from the NASA Jet Propulsion Laboratory\u27s (JPL\u27s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, Louisiana, USA

Runway Detection Using Unsupervised Classification

Recent advances in the field of remote sensing technology have opened new prospects and impelled the way of analyzing images from remote sensing satellites to detect or identify an object, or a place which is selected as area of interest. The detection of airport becomes a motivating topic recently because of its applications and importance in military and civil aviation fields. This paper presents an approach for airport detection using remote sensing images by implementing conventional K-means unsupervised classification and implementing unsupervised classification based on decomposed polarimetric features that includes Entropy (H), Anisotropy (A), and Alpha angle (α). The obtained preliminary results reveal that classification based on decomposed polarimetric features provided better results than the conventional unsupervised classification for the detection of target. In this work, the effectiveness of the algorithms was demonstrated using quadpolarimetric L-band Polarimetric Synthetic Aperture Radar (polSAR) imagery from the NASA Jet Propulsion Laboratory\u27s (JPL\u27s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, LA, USA

Secure data transfer through internet using cryptography and image steganography

Hacking became a big problem these days. Transmission of secure Data or communication through internet turns to be challenging due to security concerns. In order to forestall these security obstacles, we used Cryptography, Image Steganography. Over the centuries, there are numerous advancements in information security. Steganography and cryptography are the two emerging techniques for secure data transmission over years. Cryptography is the art of encrypting the text whereas Steganography is used to hide the text inside any multimedia element that appears to be nothing. Steganography makes the data secure as if any person views the file, he/she cannot sense that there is some secret message hide in that, human senses cannot detect or sense the data inside the multimedia element. In this paper, we proposed a system that uses both cryptography and steganography to ensure two levels of security to the data. The purpose of this paper is to develop new methodology using XOR operation for encrypting the data and embedding the encrypted data into the image pseudo randomly using user chosen key

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small

Detection of Anomalies On Earthen Levees With and Without Feature Extraction Using Synthetic Aperture Radar Imagery

Early detection of anomalies on earthen levees by a remote sensing approach could save time and cost versus direct assessment. In this paper, we implemented the support vector machine (svm) supervised classification algorithm with and without feature extraction. Features were extracted using grey level co-occurrence matrix (glcm) features using phase and magnitude imagery of polarimetric synthetic aperture radar (polsar) for the identification of anomalies on levees. The effectiveness of the algorithms is demonstrated using fully quad-polarimetric l-band synthetic aperture radar (sar) imagery from the nasa jet propulsion laboratory\u27s (jpl\u27s) uninhabited aerial vehicle synthetic aperture radar (uavsar). The study area is a section of the lower mississippi river valley in the southern usa, where the us army corps of engineers maintains earthen flood control levees