The classification of remote-sensing images based on multiple information sources offers a consistent method for the
automatic cartography of forest stands. However, fusion models reveal problems of combinatorial explosion due to the
calculation of the assignment functions. This article proposes an information-fusion approach that responds to the need
for updating the forest inventory, based on belief theory. It illustrates a solution that overcomes the problem of
combinatorial explosion that arises with the evaluation of evidence-mass functions which are used as the frame of
discernment events. This solution is based on a refinement of the frame of discernment based on the determination of all
focal elements (singleton or composite hypothesis of non null masses). Thus, the combination of information source
masses would involve only the focal elements masses. In the approach proposed here, the notions of fuzzy logic and
possibility theory have been used for the calculation of masses and combinations between classes as an intermediary
phase in arriving at belief functions. The result of the application of our fusion approach revealed a significant
improvement in optimizing the calculation of mass evidence functions and thus achieving a satisfactory classification.
At the beginning of next year (2002), CNES will launch the SPOT 5 satellite. Before the launching, CNES has to validate the choices made for the sensors with the use of simulated images. Our study has been made as part of the use of SPOT 5 panchromatic images for urban area analysis. The objective of this study is to extract built-up urban areas and urban thoroughfares from simulated images. Looking at SPOT 5 panchromatic simulated images shows us that the grey level is not a good characteristic to discriminate buildings of the urban areas from other types of regions such as thoroughfares or nude-grounds. In the meantime, buildings define heterogeneous areas whereas thoroughfares and nude-grounds are homogeneous regions. So in order to classify built-up urban areas and thoroughfares, we suggest to use the directional variance of the image. With this end in view, we define a new operator of texture analysis, and we propose to combine it with classification algorithms and edge extraction operators. In order to validate our method, we present some results we have obtained on the city of Strasbourg, using simulated SPOT 5 images of 5m per pixel resolution, provided by CNES agency.
Geographic Information System (GIS) are often old and so, some geographic elements are not represented. From satellite images and/or aerial images, we can detect cartographic elements to integrate them in the GIS and then upgrade it. Making it manually is a very long and tedious work, so computer based methods are needed. This paper presents several specific and automatic or semi-automatic methods to detect and identify several types of cartographic elements. These methods are fast and very efficient. A result evaluation is given to permit a manually correction for the non-confident elements.
Detection of geographic elements on images is important in the perspective of adding new elements in geographic databases which are sometimes old and so, some elements are not represented. Our goal is to look for linear features like roads, rivers or railways on SPOT images with a resolution of 10 meters. Several methods allow this detection to be realized and may be classified in three categories: (1) Detection operators: the best known is the DUDA Road Operator which determine the belonging degree of a pixel to a linear feature from several 5 X 5 filters. Results are often unsatisfactory. It exists too the Infinite Size Exponential Filter (ISEF), which is a derivative filter and allows edge, valley or roof profile to be found on the image. It can be utilized as an additional information for others methods. (2) Structural tracking: from a starting point, an analysis in several directions is performed to determine the best next point (features may be: homogeneity of radiometry, contrast with environment, ...). From this new point and with an updated direction, the process goes on. Difficulty of these methods is the consideration of occlusions (bridges, tunnels, dense vegetation, ...). (3) Dynamic programming: F* algorithm and snakes are the best known. They allow a path with a minimal cost to be found in a search window. Occlusions are not a problem but two points or more near the searched linear feature must be known to define the window. The method described below is a mixture of structural tracking and dynamic programming (F* algorithm).
Automatic analysis of remote sensing images faces different problems: context diversity, complexity of information. To simplify identification and to limit the search space, we use extra data and knowledge to help the scene understanding. Diversity and imprecision of information sources generate new problems. The fuzzy logic theory is used to solve the problem of imprecision. Many extraction algorithms are used to provide a more reliable result. Extraction may be performed either globally on the whole image or locally using information of data bases. Each extractor produces a map of certainty factors for a given type of geographic features according to their characteristics: radiometry, color, linear, etc. Maps contain wrong detections due to imperfections of the detectors or non- completeness of generic models. So, we generate a new map using fusion to have a best credibility used to compute a dynamic programming. It finds an optimal path even if the linear feature is partially occluded. But the path is generally erratic due to noise. Then a snake-like technique smooth the path to clean the erratic parts and to tune the level of detail required to represent the geographic features on a map of a given scale. The result is used to update data bases.
The urban areas represent a vast subject in image interpretation. It is interesting to be able to analyze town development, to make streets maps automatically or just, to mask the urban areas in satellite images. The objective of this study is to extract urban areas from remote sensing images and to make a classification of these areas. The proposed method combines different types of operators. At first, we define automatically a mask of the urban areas by combining a classification algorithm with edge extraction algorithms. Then these urban areas are segmented according to the streets, railways and rivers to obtain urban districts. Finally, we define a measure of urban density. In this paper, we focus on the urban extraction algorithm and the urban segmentation process. This study is performed by IRIT and has been partially funded by CNES agency.
The study of urban area is an important problem in image interpretation. It is interesting to be able to analyze town development on satellite images or to mask urban areas automatically. The method we present in this paper consists in the extraction of urban areas form remote sensing images and the classification of these areas. We separate the urban areas from the other types of regions. Then we classify hem according to a measure of the urban density. The algorithms we use, combine different types of operators in order to improve the final classification.
Spatial evolutions of the anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in research about the environment. This evolution can present a large preoccupation in space accommodation and environmental domains, and it gives rise to a considerable problem in terms of prospective. How will be the conditions of a region area, between now and 15, 30, or 50 years? In fact, the time consists of hierarchical events and can produce transformations upon a terrain landscape as emergence, disappearing, union of spatial entities. These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area which reveals these spatial transformations. For these purposes, we have developed a specific spatio-temporal prediction approach. The idea we present here takes a first step in attacking this problematic, it turns out very interesting results in this domain. We describe in this paper a method for analysis and prediction of terrain landscape for an established date. This method is founded on n geographic maps representing the terrain conditions for distinct years. The basic idea is to employ the observation of the temporal phenomena evolution. In fact, results of this observation represent the evolution of each region area on maps in the course of time. The evolution modeling of the regions is obtained with the help of a sequence of aerial photographies compared through different dates. It allows the geographer interested in environmental prospective problems to get type cartographical documents showing the future conditions of a landscape. This method makes use of vectorial geographic data and it achieves a prediction by means of different comparisons between attributes of regions such as the surface, center and distance between regions. The final shapes and positions of the regions are determined by combining the results stemming from applications of a linear regression method and from mathematic morphology in vectorial domain. The implemented approach model the evolution of the forest in a region of the south of France by using maps for the years 1942, 1962, and 1993. We used this method to study a region located in the Ariege mountains called 'Soulave' to describe the evolution of its landscape for the years 2000, 2005, 2010, 2015, and 2020. The experimental tests have shown promising results.
The study of urban area is an important problem in image interpretation. It is interesting to be able to analyze town development on satellite images or to mask urban areas. The objective of this study is to extract urban areas from remote sensing images and to make a classification of these areas. The detection algorithm combines different types of operators in order to improve the final detection. We separate urban areas from the other type of regions (vegetation, rivers . . .). Then the urban areas are classified into various under-classes (dense urban areas, suburbs . . .). This study has been performed by IRIT in the frame of the CNES program on studies and research on automatic analysis and interpretation of SPOT images.
In the framework of an image interpretation system for automatic cartography based on remote sensing image classification improved by a photo interpreter knowledge, we developed a system based on neural networks which simultaneously produce fuzzy rules, with their linguistic approximation as well as final classification. This paper describes the succession of steps used with this aim in view. Particularly it investigates the application of mutual information criteria to simplify fuzzy rules.
Our research takes place within the framework of an expert system for automatic remote sensing image interpretation where we have to computer pixel's topographic context [DES 89]. The aim of this paper is to present a new method to locate drainage network from a digital elevation model and to compute its topographical and topological attributes. The resulting drainage network is represented by a pixel width network, such as the gray level at each network pixel represents valley's topographical attributes.