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 161 finding their physical borderlines to other landuses like forests or agricultural areas. Texture analysis of panchromatic satellite imagery is one promising method in heterogeneous built-up settlement areas, because texture is a main feature to characterise them. Several methods have been developed to describe, classify and segment texture (Rosenfeld, 1998). Simple statistical approaches using mean or standard deviation do not take into account the spatial distribution of pixels. Several authors (Haralick and Shapiro, 1992) suggested to use co-occurrence matrices (2 nd order statistics) considering the spectral as well as the spatial distribution of the image grey values. Based on the utilization of these matrices, Steinnocher (1997) developed a method in order to demarcate settlement structures from other landuses, which tries to make the calculated texture feature images independent from directional influences within the image. Sindhuber and Jansa (1998) apply the Forstner operator in order to distinguish between point features, significant edges and homogenous areas, combine the results with a modified multispectral classification and a thresholding and achieve a classification accuracy of more than 90 %. Initiated by the Centre of Earth Observation (CEO) within the ATLAS-project, a multiple step procedure for classifying urban areas applied on IRS-1C data (test area: Berlin) has been developed (see web-site of VRS GmbH, Leipzig: www.kayser-threde/vrs/vrs.htm). Two additional texture channels were calculated using a morphological filter as well as a variance filter with a 3x3 window. Within the following segmentation process, the local contrast for each pixel is calculated and a watershed segmentation is carried out. The obtained results are integrated in an unsupervised classification. Busch (1998) describes a method by means of which built-up areas are extracted using the high spatial density of short linear features in panchromatic satellite images. Built-up and non- built-up areas are separated by a threshold, estimated from the calculated feature density in training sites stored in an existing GIS. Dowman (1998) reports on the ARCHANGEL project, which aims at developing a system for automatic registration of satellite data to maps. Within this project, segmentation algorithms have been developed, tested and integrated in the environment XPECT. 3. FUSION OF IRS-1C PAN AND LISS 3.1. Used and Tested Fusion Techniques Pixel-by-pixel image fusion aims at producing image products that are better suited for a given interpretation task. Spatial resolution and colour rendition play a decisive part in this. Without sacrificing the colour information required for object interpretation, high spatial resolution can be achieved by combining high resolution panchromatic images with multispectral ones. Important colour composites are real-colour and infrared representations (the latter particularly so for assessments of vegetation). Two different requests are usually pursued with the fusion of satellite imagery. On the one hand, it can be the improvement of the visual ability to interpret the image data. Here, the focus is primarily on the geometrical structural image characteristics. On the other hand, the goal can be the maximum preservation of the spectral information and thus, the maximum agreement of the fusion product with the original multispectral images. It was shown that a maximum preservation of both described image characteristics is only hardly possible (de Bethune et al., 1998). Nevertheless, methods are being tested, which ensure the optimal preservation of the spectral and the geometri cal-structural information in the synthetic fused image (Raptis et al., 1998). A further important aspect is achieving a good spectral and spatial quality of the fusion product for different landcover types and applications, and different possible spectral channels (Zhang and Albertz, 1998). This target is also pursued in this investigation using selected methods for fusion of IRS-C images. The stability of the procedures is checked by the use of different test areas. The produced fused images are compared visually and statistically regarding their spatial and spectral characteristics. Among the various methods for fusion of multispectral with panchromatic satellite data described in the literature, some classical and some latest techniques were selected and checked whether they were suitable for the given objective using IRS-1C data. The RGB to IHS color space transformation (IHS), the transformation procedure by means of principal component analysis (PCA) and the Brovey fusion method (B) belong to the classical fusion methods. 'Local Mean and Variance Matching’ (LMM and LMVM) and high-pass filter techniques (HPF) (de Bethune et al., 1998) belong to the latest techniques. Table 1. Used and tested fusion techniques. Image fusion technique Explanation IHS Fusion by means of RGB to IHS color space transformation WTA Modified RGB to IHS color space transformation (Carper et al., 1990) PCA Fusion bv principal component analysis Brovey Fusion using arithmetic combination of bands LMM Fusion by means of local mean LMVM Fusion by means of local mean and standard deviation HPF Fusion by means of high-pass filtering 3.2. Visual Control The following conclusions can be drawn after a visual comparison of the tested fusion techniques: - The PC technique, and to a certain extent also the IHS technique, strongly depend on the spectral overlap of the original data. This overlap is not given with the IRS sensor due to the spectral band positions (NIR, Red, Green). Thus,

Access restriction

Copyright

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

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation