G. Bo

SAR images and interferometric coherence for flood monitoring

SAR backscatter intensity has already been successfully exploited for the detection of different types of changes in a scene, including seasonal changes, ice floes, landslides, earthquake damage and flooding. A drawback is that the backscatter intensity is affected by the presence of wind fields, water. Flood detection performed by only involving SAR intensity values could be not reliable, unless the weather information is integrated. On the contrary, interferometric coherence, usually low in presence of water, is not sensitive to weather conditions. The authors demonstrate that water can be easily identified by a data fusion which involves multitemporal backscatter intensities and multitemporal interferometric coherence: the additional information provided by the absence of coherence over water allows for a more accurate identification of the flooded areas with respect to simply exploiting the backscatter intensity.

A locally adaptive approach for interferometric phase noise reduction

This paper presents a new filtering technique for SAR interferometric phase images. Due to the presence of noise in the data, a filtering step must be performed before unwrapping the interferogram in order to obtain a more accurate evaluation of the true phase values and, as a consequence, a better topographic model. The goal of this technique is to reduce the phase noise that has an additive gaussian model, preserving the local contrast and the shape of the fringes. Noise reduction should be realized involving an adaptive approach that takes into account the aspect and the local statistics of the interferometrical image. The authors propose an edge-preserving method that exploits the local content of the phase image in order to determine for each pixel the best matching shape of the filtering mask, searching in a set of different neighbourhood systems. To test whether a proposed mask is efficient or not, the local variance is computed for each neighbourhood a proposed mask shape at a certain pixel position is accepted only if its variance value is lower than those of alternative shapes. Once the best mask has been chosen, a nonlinear adaptive filtering function can be applied. The effectiveness of this technique can be seen in a considerable reduction of the interferometric noise, while preserving the integrity of phase gradients, i.e., the contrast and the morphology of fringes.

Coastline extraction in remotely sensed images by means of texture features analysis

An innovative technique for the detection of the coastline in remotely sensed images is presented. In a previous version of the method a connectivity map was computed by simply considering the grey levels and the physical distance between pixels in the image. The results obtained by processing different kinds of remotely sensed data clearly showed that it is possible to correctly detect the shoreline position only if the sea portion is really homogeneous. By using this approach, problems arise when sources of non-homogeneity are present in the image. An improved version of the method has been implemented, which involves texture features, instead of the simple gray level, in order to compute the connectivity map. By operating in this way it is possible to better take into account the spatial variability of data as an information source.

Semiautomatic coastline detection in remotely sensed images

Regarding the application of remote sensing and related data processing methodologies to the analysis and monitoring of the coastal environment, an innovative technique for the extraction of the coastline from space borne or aerial images has been developed. The interactive method proposed is based on the involvement of the local contextual information which is always present in remotely sensed data. The exploitability and reliability of the algorithm for the shoreline identification have been tested by processing images from different sensors. Some of the obtained results are shown.

Locally adaptive noise filtering approach for phase-unwrapping improvement

In order to obtain a more accurate topographic model, a noise filtering step must be performed before the unwrapping of phases. We propose an iterative process that involves a filter in the spatial domain and a multi-resolution phase unwrapping method. The approach is based on the generation of an approximate phase model, which is iteratively refined. In the aim of preserving fine details in the interferogram that are directly related to the ground topography, an edge- preserving smoothing method has been applied. The local spatial content of the phase image is exploited in order to determine for each pixel the best matching shape of the filtering mask, searching in a set of different neighborhood systems. Then, a non-linear adaptive filtering function, based on the local estimation of noise and signal standard deviation, is adopted. The results obtained by processing several noisy simulated and real interferometrical images with the described method show clearly how the combination of a detail preserving adaptive filter with a noise robust phase unwrapping approach appears as a good means for reducing the influence of distributed noise and low coherence areas on the determination of a digital ground elevation model.

Improvements in flood monitoring by means of interferometric coherence

SAR images has already been successfully exploited for the detection of changes in a scene. Being the backscatter intensity affected by the presence of wind fields, water and flood identification could be unreliable, unless weather information are integrated. On the contrary, interferometric coherence, usually low in presence of water, is not sensitive to weather conditions. As a consequence, the additional information provided by the absence of coherence over water should allow a more accurate identification of flooded areas. A qualitative and quantitative evaluation of the improvement that could be obtained by exploiting interferometric data has been performed. The data set is composed by interferometric pairs acquired by the ERS-l/ERS-2 satellites before and during the Yangtze River flooding occurred in China in summer 1998. Starting from these data, several features have been computed and associated to the channels of an RGB image, in order to obtain an intuitive interpretation of the data content and an easy identification of the flooded areas. The results show that, in order to correctly highlight the flooded areas the best combination of features includes the coherence difference between the acquisitions before and during the flooding, the backscatter intensity in the reference period and the coherence computed during the flooding.

Issues in geographic data quality assessment by remote sensing techniques

The aim of this paper is to give considerations and suggestions that are relevant for the development of methods for the detection, updating, and evaluation of road extractors. This is done by presenting an example in which the quality of an existing road network is measured against image data. The focus of this paper is on practical issues, creating a link to fields that perhaps are not of direct importance to the image analysis community. This can help people to understand better the context in which the remote sensing image analysis takes place and hope it can be improved through the development of technologies and tools for mainstreaming the use of geographic information also for info-mobility services, including large-scale heterogeneous and distributed collection of geo-spatial data and for creating a sustainable landscape for geographic data creation, use, management and publishing.

Texture features analysis for coastline extraction in remotely sensed images

The accurate knowledge of the shoreline position is of fundamental importance in several applications such as cartography and ships positioning1. Moreover, the coastline could be seen as a relevant parameter for the monitoring of the coastal zone morphology, as it allows the retrieval of a much more precise digital elevation model of the entire coastal area. The study that has been carried out focuses on the development of a reliable technique for the detection of coastlines in remotely sensed images. An innovative approach which is based on the concepts of fuzzy connectivity and texture features extraction has been developed for the location of the shoreline. The system has been tested on several kind of images as SPOT, LANDSAT and the results obtained are good. Moreover, the algorithm has been tested on a sample of a SAR interferogram. The breakthrough consists in the fact that the coastline detection is seen as an important features in the framework of digital elevation model (DEM) retrieval. In particular, the coast could be seen as a boundary line all data beyond which (the ones representing the sea) are not significant. The processing for the digital elevation model could be refined, just considering the in-land data.

Content-based retrieval for remotely sensed images

The work describes an innovative technique to automatically extract and manage remote sensing image-content. Simple but very flexible numeric recognition methodologies allow the content-based retrieval from huge remotely sensed image database. The most important result of this methodology is a tool for the information retrieval based on example. In order to properly characterize remotely sensed images and improve retrieval performance, many factors, such as the image resolution, the scale, the sensor features, have been taken into account. Kingfisher is the content-based database management system, developed at DIBE laboratories, that exploits these methodologies.

Linear feature extraction for the detection and updating road networks

The article discusses cost reduction in the quality assessment of digital cartographic information by means of satellite images. It provides some generic comments on aspects involved in the quality of digital vector data following a schema in use by the USGS, and gives an example of how Landsat 7 data can be useful to assess the spatial accuracy of road networks.