Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: OPERATIONAL APPLICATIONS OF MULTI-SENSOR IMAGE FUSION. C. Pohl, H. Touron

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 126 Due to the frequent cloud cover, visual imagery for the area of interest is often not available. In addition, the re-visit time of the individual satellites alone is not sufficient to monitor fast changes, e.g. as they occur in a harbour such as Rotterdam, the Netherlands. Using an image of the relatively high resolution Indian Satellite IRS-1C PAN (~ 6 m), the interpretation of an oil storage facility containing tanks with floating roofs has been performed. The cloud-free image depicts the level of fluid available in the different tanks. In case of clouds, this information cannot be obtained. Since SAR sensors are active instruments with a wavelength that can penetrate clouds, they can acquire images at any time of the day or year. For comparison, a multi-temporal composite of three different Radarsat images has been visually analysed. It leads to the assumption that the backscatter returning to the sensor is stronger, the lower the fluid level in the tank. The explanation lies in the increased comer reflector effect with decreasing fluid level (see Fig. 2). Using more imagery and measurements of the point targets in the SAR images could lead to the establishment of a relationship between strength of backscatter and level of oil storage. The available knowledge of the location of the tanks, based on one cloud-free optical image, facilitates the approach. In another case, a SAR image has been used to study the difference of tank covers. The tanks had been identified from optical imagery, which was not sufficient to distinguish floating from conical roofs. The SAR on the other hand contributed this information due to the differences in backscatter from the different types of roofs. Fig. 2. Backscatter from filled (left) and almost empty (right) oil tank. The experience at the WEUSC using visual image interpretation as major exploitation element has proven that image fusion plays a vital role in the facilitation of feature detection, recognition and identification. The main criterion for the choice of images for image fusion is the contribution of complementary information contained in each individual image. If the operational process of image fusion succeeds to maintain the information contribution of the individual images in the fused product, it is not necessary to evaluate the individual images alone. The image analyst can understand the feature and its context much faster in the fused product than looking at the individual images. This speeds up the exploitation process and improves the reliability of the obtained interpretation results. Image fusion does not produce information that is not already contained in the original images. But it forms a mean to enhance certain features and their environment in order to draw the attention of the human interpreter to relevant aspects. The most time consuming part in pixel-based image fusion is the image registration, i.e. the identification of GCPs. This is especially true for spatially very different data or VER/SAR registration. A special registration tool has been developed to address this part of the processing chain. This tool decreases the time and accuracy needed for registration, because it allows not only GCPs, but also linear or area features, in the establishment of the geometric model. Furthermore, it introduces a standard in the processing chain, which is essential in an operational environment. In parallel, the WEUSC allocates resources to research and technical development focusing on an automation of this process. A first prototype exists integrating sensor models and a priori information provided with the image data. At the moment the systems can process SPOT, IRS-1, MK-4 and Radarsat imagery. A major element of the operational implementation of image fusion with respect to visual image interpretation is the interactive component. The operator needs to be capable of empirically tuning individual parameters involved in the fusion process or in the enhancement of input data as well as the end product. 4. RESULTS Operational image exploitation means the achievement of speed and quality, as well as reliability of results. However, quality and accuracy should be seen in the context of requirements. Depending on the application and time constraints resulting from the operational environment, images will not always be processed to the highest accuracy level. The approach and complexity of image processing is defined on the basis of the needs expressed. This is very important for an optimization of resources. A problem that often reduces the speed of processing is the lack of availability of ancillary data describing the imagery as well as ground truth. The image analyst uses the fusion approach with care to fully understand the nature of the fused product in order to draw proper conclusions. 5. CONCLUSIONS The experiences gained show very clearly that a major element of the operational implementation of image fusion with respect to visual image interpretation is the interactive component. The operator needs to be capable of empirically tuning individual parameters involved in the fusion process or in the enhancement of input data as well as the end product. The fine tuning of the image enhancement parameters, i.e. histogram value distribution, filter, assignment of colours etc., influences the success of the fusion itself. A small interactive window, containing a representative subset of the image to be processed, has proven to be an excellent aid to support the determination of the ’right’ values for the parameters. This window shows in real time the effect of changes in the parameters on the input data or the fused product, depending on the selection of the data to be

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