Penélope López-Quiroz

Mexico City Subsidence Measured by InSAR Time Series: Joint Analysis Using PS and SBAS Approaches

In multi-temporal InSAR processing, both the Permanent Scatterer (PS) and Small BAseline Subset (SBAS) approaches are optimized to obtain ground displacement rates with a nominal accuracy of millimeters per year. In this paper, we investigate how applying both approaches to Mexico City subsidence validates the InSAR time series results and brings complementary information to the subsidence pattern. We apply the PS approach (Gamma-IPTA chain) and an ad-hoc SBAS approach on 38 ENVISAT images from November 2002 to March 2007 to map the Mexico City subsidence. The subsidence rate maps obtained by both approaches are compared quantitatively and analyzed at different steps of the PS processing. The inter-comparison is done separately for low-pass (LP) and high-pass (HP) filtered difference maps to take the complementarity of both approaches at different scales into account. The inter-comparison shows that the differential subsidence map obtained by the SBAS approach describes the local features associated with urban constructions and infrastructures, while the PS approach quantitatively characterizes the motion of individual targets. The latter information, once related to the type of building foundations, should be essential to quantify the relative importance of surface loads, surface drying and drying due to aquifer over-exploitation, in subsoil compaction.

SAR image classification with a directional-oriented discrete Hermite transform

This paper presents a novel classification scheme for SAR images based on the perceptual classification of image patterns in the Discrete Hermite Transform (DHT) domain over a roughly hexagonal sampling lattice. The DHT analyzes a signal through a set of binomial filters which approximate the Gaussian derivatives with the advantage that they are computed efficiently. In order to obtain the DHT referred to a rotated coordinate system the set of coefficients of a given order are mapped through a unitary transformation that is locally specified. Such a transformation is based on the generalized binomial functions so that the rotation algorithm is efficient too. This representation allows a perceptual classification, which is achieved by thesholding the approximation errors that are obtained under the hypotheses that the underlying pattern is a constant (0-D), an oriented structure (1-D) or a non-oriented structure (2-D). The threshold is based on light adaptation and contrast masking properties of the human vision.

Long Term Subsidence Analysis and Soil Fracturing Zonation Based on InSAR Time Series Modelling in Northern Zona Metropolitana del Valle de Mexico

In this study deformation processes in northern Zona Metropolitana del Valle de Mexico (ZMVM) are evaluated by means of advanced multi-temporal interferometry. ERS and ENVISAT time series, covering approximately an 11-year period (between 1999 and 2010), were produced showing mainly linear subsidence behaviour for almost the entire area under study, but increasing rates that reach up to 285 mm/yr. Important non-linear deformation was identified in certain areas, presumably suggesting interaction between subsidence and other processes. Thus, a methodology for identification of probable fracturing zones based on discrimination and modelling of the non-linear (quadratic function) component is presented. This component was mapped and temporal subsidence evolution profiles were constructed across areas where notable acceleration (maximum of 8 mm/yr2) or deceleration (maximum of −9 mm/yr2) is found. This methodology enables location of potential soil fractures that could impact relevant infrastructure such as the Tunel Emisor Oriente (TEO) (along the structure rates exceed 200 mm/yr). Additionally, subsidence behaviour during wet and dry seasons is tackled in partially urbanized areas. This paper provides useful information for geological risk assessment in the area.

SAR-image classification with a directional-oriented discrete Hermite transform and Markov random fields

A novel classification scheme for SAR images based on the perceptual classification of image patterns in the discrete Hermite transform (DHT) domain over a roughly hexagonal sampling lattice has been developed. In order to obtain the DHT referred to a rotated coordinate system the set of coefficients of a given order are mapped through a unitary transformation based on the generalized binomial function. This representation allows a perceptual classification, including constant patterns (0-D), oriented structures (1-D), and non-oriented structures (2-D). Classification is based on light adaptation and contrast masking properties of the human vision. Finally, classification is improved by means of a probabilistic approach based on Markov random fields.

Markovian regularization of Hermite-transform-based SAR image classification

A novel classification scheme for SAR images based on the perceptual classification of image patterns in the Discrete Hermite Transform domain has been developed. In order to obtain the DHT referred to a rotated coordinate system the set of coefficients of a given order are mapped through a unitary transformation based on the generalized binomial function. This representation allows a perceptual classification, including constant patterns (0-D), oriented structures (1-D), and non-oriented structures (2-D). Classification is based on light adaptation and contrast masking properties of the human vision. Finally, classification is improved by means of a probabilistic approach based on Markov Random Fields.