This paper reports the operational implementation of radargrammetry for the production of Digital Elevation Models, or DEMs, to areas of rugged topography. The Southern Ethiopian Highlands east of lake Abaya, with elevations between ca. 900 and 4,400 meters, were mapped. Currently available topographical maps are of insufficient quality to assist a study of the area's unique land use system, which is arguably the oldest and most durably sustained land use system of the planet. Without external inputs or terracing, the land use system maintains soil fertility and staves-off hunger. It has been doing so during the past 30 years of unrest and civil war, in one of the most crowded regions of Ethiopia. However, the central role of the staple crop enset within the land use system and its production cycles has hardly been the subject of scientific study. Understanding of this system is most likely to be relevant to enhancement of health and productivity in many regions of the world. Upon the request of the Agricultural Bureau for Gedeo Zone, geocoded and georeferenced topographical maps with accuracy of 20 meters (x, y and z) were made by PRIVATEERS N.V. on the basis of RADARSAT multi-incidence (S2/S7) images. These maps are now incorporated as the basic layer within the Bureau's GIS system. Map production techniques proved to be cost effective and relevant; especially for mountainous areas with poor accessability where correct geographic information is not available. The ease of orientation proved of invaluable help to rationalize execution and planning of cost-effective environmental field work and reporting.
On January 13th 2001, a very strong earthquake struck El-Salvador, causing almost 1000 deaths and huge destruction, leaving more than one million people homeless. As support to the rescue teams, a project was initiated to provide up-to date maps and to identify damages to housing and infrastructures, covering the whole country. Based on the analysis of SPOT Panchromatic satellite imagery, updated maps were delivered to the rescue teams within 72 hours after the earthquake. In addition, during the 10 days following the earthquake, high resolution mapping of the damages was carried out in cooperation and coordination with rescue teams and relief organizations. Some areas of particular interest were even processed and damage maps delivered through the Internet, three hours after the request. For the first time in the history of spaceborne Earth observation, identification and evaluation of the damages were delivered on-site, in real-time (during the interventions), to local authorities, rescue teams and humanitarian organizations. In this operation, operating 24 hours a day and technical ability were the keys for success and contributed to saving lives.
12 This paper reports the operational implementation of new techniques for the exploitation of remote sensing data (SAR and optical) in the framework of forestry applications. In particular, we present a new technique for standing timber volume estimation. This technique is based on remote sensing knowledge (SAR and optical synergy) and forestry knowledge (forest structure models), proved fairly accurate. To illustrate the application of these techniques, an operational commercial case study regarding forest concessions in Sarawak is presented. Validation of this technique by comparison of the remote sensing results and the database of the customer has shown that this technique is fairly accurate.
The states of Honduras, Nicaragua, and El Salvador have been toughly hit by the “Mitch” hurricane in the first days of November 1998. The extent of damages due to this hurricane, as well as their impact upon local economy were exceptional. In this framework, a remote sensing project was scrambled to provide a large scale evaluation of the damages suffered by the three countries. To reach this objective, new remote sensing products called DYNAMIC products have been designed. These products are, based on using, either RADAR satellite images, or Optical/RADAR satellite data fusion. The most efficient techniques have been applied to produce these products, thus enabling change detection at a very fine spatial scale (10x10 meters). Project schedule and operations, as well as the validation of its products are reported in this paper.
Five new Distribution-Energy Maximum A Posteriori speckle filters are established for the following cases: single detected, multilook multi-channel detected, single look complex SAR images, separate complex looks, and fully polarimetric SAR data. As shown, these new filters are particularly efficient to reduce speckle noise, while preserving textural properties and spatial resolution, especially in strongly textured SAR images.
The legendary 'Ciudad Blanca' of Honduras was first referred to under the name Xucutaco by the Spanish conquistador Hernan Cortes already in 1526. Located in the remote, impenetrable and incompletely mapped rainforest of the Mosquito Coast, it was never conquered by the Spanish. With the time, it was slowly abandoned and forgotten. Two JERS-1 and one ERS-2 SLC Synthetic Aperture Radar (SAR) images have been used to identify and to locate the lost city, a task made difficult due to the thick vegetation cover. To this end, advanced processing tools for the detection of artificial targets under forest cover, and for SAR data fusion have been used. Among the techniques used, a new Bayesian Distribution Entropy Maximum A Posteriori (DE-MAP) vector speckle filter, particularly suited for the restoration of a strongly textured scene, has been used to enhance the SAR images. This new speckle filter incorporates a statistical description of the effects of the SAR imaging system: in order to account for the effects due to the spatial correlation of the speckle in SAR images, an estimator originating from the local spatial autocorrelation function (ACF) of the SAR signal are incorporated to this filter, to refine the evaluation of the non-stationary first order local statistics, to improve the restoration of the scene textural properties, and to preserve the useful spatial resolution in the speckle filtered image. On the other hand, radargrammetric techniques have been used to: (1) produce a Digital Elevation Model (DEM) of the study area; (2) fuse ERS and JERS information in order to allow visual identification of the remnants of Ciudad Blanca by visual photo-interpretation. Using the processed images, geocoded UTM spatio-maps of the region have also been produced to locate accurately our findings, and guide a ground expedition in the future.
KEYWORDS: Speckle, Image filtering, Synthetic aperture radar, Control systems design, Soil science, Digital filtering, Image fusion, Control systems, Spatial resolution, Data fusion
Two new Bayesian Maximum A Posterior vector speckle filters are developed for multi-channel detected synthetic aperture radar (SAR) images. These filters incorporate statistical descriptions of the scene and of the speckle in multi- channel SAR images. These filters incorporate statistical descriptions of the scene and of the speckle in multi- channel SAR images. These models account for the scene and system effects which result in the presence of a certain amount of correlation between the different channels. In order to account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local autocorrelation functions are incorporated to these filters, to refine the evaluation of the non-stationary first order local statistics, to improve the resolution of the scene textural properties, and to preserve the useful spatial resolution in the speckle filtered image. Since the new established Bayesian speckle filters present the structure of control system, their application is the first processing step of application-oriented control system designed to exploit the synergy of SAR sensors. We present here such a control system, designed to retrieve soil roughness and soil moisture through Bayesian ERS/RADARSAT data such a control system, designed to retrieve soil roughness and soil moisture through Bayesian ERS/RADARSAT data fusion. Results obtained on a couple of ERS PRI and RADARSAT standard beam SAR images show that the new speckle filters present convincing performances for speckle reduction, for texture preservation and for small scene objects detection. The retrieval of soil roughness and soil moisture through Bayesian data fusion of ERS and RADARSAT data provides also valuable results for the monitoring of agriculture and environment.
Two new Bayesian Maximum A Posteriori (MAP) vector speckle filters are developed for multi-channel detected SAR images. These filters incorporate statistical descriptions of the scene and of the speckle in multi-channel SAR images. These models account for the scene and system effects which result in the presence of a certain amount of correlation between the different channels. In order to account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local autocorrelation functions are incorporated to these filters, to refine the evaluation of the non-stationary first order local statistics, as well as to improve the restoration of the scene textural properties and to preserve the useful spatial resolution in the speckle filtered image. Results obtained, first on 3-look spaceborne ERS PRI multi-temporal images, then on a couple of ERS PRI and RADARSAT standard beam SAR images illustrate the performance of these estimators for different SAR combinations. These results show that these filters present convincing performances for speckle reduction, as well as for texture preservation and for small and/or thin scene objects detection. Finally, since the new established Bayesian speckle filters present the structure of control systems, promising perspectives are presented for the development of application oriented processing chains for multi-channel SAR images, where the speckle filtering operation will be the first processing step.
Most of the processing/analysis tools for SAR images, and particularly the most usual speckle filters, are based on the use of first order local statistics (local mean and local variance). In order to account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local autocorrelation functions (ACF) are used, to refine the evaluation of the non-stationary first order local statistics, as well as to detect the structural elements of the scene. The aim is to enhance scene textural properties, and to preserve the useful spatial resolution in the speckle filtered image. To detect and preserve very thin scene structures in the presence of speckle, an heuristic implementation of these estimators is presented for the case of multilook SAR images. Results obtained on 7-look airborne C-SAR and 3-look spaceborne ERS PRI images with different spatial resolutions illustrate the performance of these estimators, either implemented in the speckle filter, or for texture analysis, or for small/thin scene objects detection. Finally, it is shown how two-points statistics and derived indices can be used as texture analysis tools or as discriminators. Some ERS applications using these techniques, either for speckle filtering, or for texture-based analysis, are briefly presented.
We consider SAR segmentation as an important step for the operational use of satellite SAR imagery in routine mapping exercises. The use of multi-temporal SAR imagery in this respect is of specific interest in areas where optical data are difficult to obtain, due to prevailing weather conditions. For areas where timely optical data are available, a hybrid approach can be adopted, still using the same segmentation algorithm described in this paper. In this paper we present the results of the application of a generic segmentation method on multi-temporal ERS-1 SAR imagery of the Dutch Flevoland agricultural area. The data were recorded during the fall of 1991, and constitute a series of 7 co-registered PRI images. Before segmentation, the data are filtered, using a maximum a priori filter technique and then byte-scaled to allow segmentation of any combination of (temporal) channels. We will evaluate the various channel combinations with respect to segmentation efficiencies. The results are compared to an existing data base of fixed field boundaries and a vector map of 1991 field boundaries derived from optical data sets (SPOT). Later we will compare the quality of field averaged PRI data extracted with polygons generated in the segmentation procedure with that from manually digitized field boundaries. One of our final objectives is to automatically generate multi-temporal backscattering signatures for the training of both supervised classification by means of neural networks [9] and supervised tillage monitoring [5]. Especially the potential to significantly advance the time of earliest estimates of crop acreage, by combining results from segmentation and knowledge based classification, is of interest in this framework.
Local second order properties, describing spatial relations between pixels are introduced into the single-point speckle adaptive filtering processes, in order to account for the effects of speckle spatial correlation and to enhance scene textural properties in the restored image. To this end, texture measures originating, first from local grey level co-occurrence matrices (GLCM), and second from the local autocorrelation functions (ACF) are used. Results obtained on 3-look processed ERS-1 FDC and PRI spaceborne images illustrate the performance allowed by the introduction of these texture measures in the structure retaining speckle adaptive filters. The observable texture in remote sensing images is related to the physical spatial resolution of the sensor. For this reason, other spatial speckle decorrelation methods, more simple and easier to implement, for example post-filtering and linear image resampling, are also presented in this paper. In the particular case of spaceborne SAR imagery, all these methods lead to visually similar results. They produce filtered (radar reflectivity) images visually comparable to optical images.
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