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:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: CLASSIFICATION OF SETTLEMENT STRUCTURES USING MORPHOLOGICAL AND SPECTRAL FEATURES IN FUSED HIGH RESOLUTION SATELLITE IMAGES (IRS-1C). Maik Netzband, Gotthard Meinel, Regin Lippold

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 163 4.2. Identification of Urban Structures by Morphological Analysis of Panchromatic Data Preliminary talks with potential users made clear that especially regional planners are very interested in landcover maps. Up-to- date information of the respective planning areas in medium scales (1 : 100,000 - 1 : 25,000) is required, to be supplied by analysis of high resolution satellite imagery. So far, the landcover is determined on the basis of topographic maps in scale 1 : 100,000 as well as by ground surveys. This data acquisition method is very time-consuming and supplies insufficient results regarding geometrical accuracy and especially up-to-date status. Two needs become obvious: firstly, a high resolution merge product, in which the high geometrical resolution of the panchromatic data is combined with the spectral resolution of the multispectral data, for a good visual analysis, and secondly a (semi)automatic landcover classification. Since the class ‘built- up areas’ represent a substantial feature for settlement dynamics and expansion, especially in urban but also rural areas, the automatic identification of built-up areas, which are to be combined if necessary into residential areas, is particularly desirable. With these specifications, the goal of this project part is the creation of a "settlement mask" in medium scales especially for regional planning. As shown above, the purely multispectral classification without inclusion of spatial-structural image information provided insufficient classification accuracies, particularly for built-up areas. Since the software ERDAS Imagine, which was so far used for image processing, does not currently provide (or only to a limited extent) tools for spatial-structural image analysis, the software HALCON was procured. This software package was developed for various applications in machine vision and is used worldwide for development, research and education purposes. It offers various processing capabilities in different applications, like remote sensing and aerial photo interpretation, production automation, quality control, medical image processing or monitoring. After importing the panchromatic image scene, grey level morphological analysis was performed using an elliptic binary structural element. In this process, a ‘top-hat’ transformation and a ‘bottom-hat’ transformation were applied to the panchromatic data. The top-hat transformation is a "peak" detector, i.e. highlights bright spots in the original data, while the bottom-hat transformation (known also as well transform) is a "valley" detector, i.e. highlights dark spots. During the top-hat transformation, a morphological opening is performed, followed by a subtraction from the original image. The opening consists of an erosion followed by a ‘Minkowski addition’ (dilation). The effect of opening is that large structures remain predominantly intact, whereas small bright structures, e.g. lines and points are reduced or eliminated. The application of the bottom-hat transformation consists of a closing followed by subtraction from the original image. Closing is defined as a dilation and a subsequent ‘Minkowski subtraction’ (erosion). Closing achieves the opposite effect, i.e. small dark structures are reduced or eliminated. These two morphological filtering procedures provide complementary representations of the built- up areas in the panchromatic image, as verified by visual analysis of the results. Theoretically, since the buildings appear usually bright, a top-hat transformation would suffice. However, especially in areas shadowed by higher buildings, the bottom- hat filtering provides a good representation of built-up areas in addition to the top-hat results. Then, the filtering results are combined into regions. Various algorithms are available for region segmentation. After detailed investigations, the ‘hysteresis thresholding’ after Canny (1983) was selected to segment regions. In this method, two threshold values are used, a lower (‘low’) and an upper (‘high’) one, for the segmentation. All pixel values in the input image that are larger or equal to the upper threshold are transferred to the output image as ‘safe’ points. All pixels with grey value less than the lower threshold are rejected (get the value 0). ‘Potential points’ with grey values between the two thresholds are finally accepted, only if they are connected to a ‘safe point’ by ‘potential points’, whereby the path length should be less than a threshold. The ‘safe points’ thus radiate in their environment, and "have a lasting effect" (hysteresis). The grey values of the input images remain unchanged, only some regions contain rejected pixels. The regions segmented with the above described procedure consist of more or less large single structures. For the creation of a settlement mask, it appeared useful to combine these structures to more compact spatial objects (larger building complexes, not an overall settlement mask). Therefore, a closing was applied to the data. Closing smooths edges of regions, merges objects separated by a thin line and closes holes smaller than the structural element, whereas individual regions, separated by a distance larger or equal to the structuring element, do not merge. 4.3. Combination of Spectral and Morphologic Image Information After performing the two classifications based on the multispectral and morphologic image information, our investigations focussed on the unification of the partial results for the creation of an accurate settlement mask. The settlement areas extracted from the panchromatic data by morphological analysis were visually checked and showed a high agreement to the actual settlements. However, they are fragmented within "closed" settlement areas, i.e. consist of individual components and do not cover the whole settlement areas. Nevertheless, it should be considered that the term "closed" settlement area can be defined in different ways and very strongly depends on the investigation scale (scale of map to be produced, monitoring scale). Topics like the size of the non-built-up areas within settlement areas which should be detected, the number and type of landcovers to be classified, the minimum size of settlement areas to be determined, are decided by the concrete user and depend on the application. Regional

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