Tesis doctoral de María Victoria Tello Alonso
In the recent years, a wide interest has been focused on research and development for the operational use of satellite remote sensing tools for earth observation. Among different types of sensors, synthetic aperture radars (sar) offer distinctive characteristics, essential for several applications. The observation capability of sar sensors is independent of the day – night cycle, of the presence of clouds and of the weather conditions in general. Nevertheless, due to the processing involved in the synthesis of sar images, automatic interpretation of sar data is awkward. For an extended operational exploitation of sar products, the development of specific unsupervised techniques for the post-processing of sar images is necessary. after analyzing the particularities of sar images, focusing in particular on the elements making their interpretation difficult, this dissertation proposes a set of post-processing techniques for oceanic sar images, based on time – frequency methods and in particular on the wavelet theory. First, a multiscale algorithm for automatic spot detection in a noisy background has been developed. It is based on the pointwise combination of wavelet coefficients of different bands at the same scale. This technique has been applied to automatic vessel detection and, more specifically, to difficult situations of detection: small fishing ships with low reflectivity. Its efficiency has been compared to other existing algorithms. After that, a method based on the pointwise combination of wavelet subbands at different scales has been proposed. It has been applied to the robust detection of frontiers and linear features. This technique has been employed for the unsupervised detection and monitoring of the coastline in sar images. Its robustness has been proven through test on a large set of images showing diverse characteristics. Then, the issue of texture analysis in oceanic sar images has been addressed. An algorithm for the estimation of the local regularity has been proposed, based on the quantification of the wavelet spectrum evolution through scales. A measure of the local fractality is derived from the local regularity. This technique is applied to the detection of oil spills in the ocean surface in sar satellite based images. the contributions of this dissertation range in two directions. On the one hand, in the direction of multiscale signal processing and, on the other hand, in the direction of automatic interpretation of sar images. For the multiscale signal processing, a different way of understanding and applying time – scale decompositions (or, equivalently, time – frequency) is proposed. In order to take more efficiently into account the information content in the projected space, the methods proposed carry out the analysis directly in the transformed domain. For the unsupervised interpretation of sar images, the suitability of the multiscale framework has been justified. Inscribed in this multiscale theory, the novel techniques proposed in this dissertation are simple, flexible, robust and self-contained.
Datos académicos de la tesis doctoral «Post-processing methods for ocean monitoring in sar images«
- Título de la tesis: Post-processing methods for ocean monitoring in sar images
- Autor: María Victoria Tello Alonso
- Universidad: Politécnica de catalunya
- Fecha de lectura de la tesis: 07/02/2011
Dirección y tribunal
- Director de la tesis
- Carlos López Martínez
- Tribunal
- Presidente del tribunal: philippe Salembier clairon
- harm Greidanus (vocal)
- Antonio María Turiel martínez (vocal)
- laurent Ferro-famil (vocal)