Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Abstract

Abstract:This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with an emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming; therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the National Aeronautics and Space Administration (NASA) Jet Propulsion Lab (JPL)'s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi River in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence (GLCM) matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT) features and GLCM features provided higher overall classification accuracies compared to the pixel-based polarimetric features.

Wiener–Granger Causality Theory Supported by a Genetic Algorithm to Characterize Natural Scenery

“…In Reference [22], the CBIR approach is presented to classify natural scenery images through the composition of relevant features in relation to the texture, like in Reference [23], the shape and distribution of the luminosity.…”

Radar and Radio Signal Processing

“…In the area of SAR processing, Dabbiru et al utilized textural and wavelet-based features from SAR imagery to detect slump slides in earthen levees [7]. Penner et al developed a 2D ground-based SAR system and associated 3D time-domain backpropagation algorithm to generate 3D radar images of trees [8].…”

“…This special issue [2] has eight papers covering a diversity of categories in radar signal processing, including three papers on radar optimization and system design [3][4][5], one paper on parameter estimation [6], three papers in the area of synthetic aperture radar (SAR) and inverse SAR (ISAR) processing [7][8][9], and one paper on harmonic radar [10].…”

Identificación y clasificación de escenas por causalidad de Wiener-Granger

“…En[56], el enfoque CBIR se presenta para clasificar imágenes de paisajes naturales a través de la composición de características relevantes en relación con la textura como en[11], la forma y la distribución de la luminosidad.Al ser una técnica de aprendizaje sin supervisión, CBIR todavía tiene algunas desventajas, ya que la información extraída solo se trata como un histograma que representa la composición de las texturas en un escenario. Esta forma de caracterizar un escenario no ha sido capaz de obtener más de 85 % de clasificación, por eso han surgido nuevas propuestas que utilizan metodologías híbridas para dar a CBIR una mayor solidez[37].En[37,61], los autores combinan la información CBIR con cierto contenido semántico introduciendo objetos conceptuales de alto nivel, tratando de vincular imágenes basadas en contenido con objetos…”