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:: COMBINING SPECTRAL AND TEXTURAL FEATURES FOR MULTISPECTRAL IMAGE CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS. H. He , C. Collet

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 175 COMBINING SPECTRAL AND TEXTURAL FEATURES FOR MULTISPECTRAL IMAGE CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS H. He , C. Collet Department of Geography, University of Fribourg, Perolles, 1700 Fribourg, Switzerland hongchang.he@unifr.ch , claude.collet@unifr.ch KEYWORDS: Artificial Neural Networks, Multispectral Image, Landcover and Landuse, Classification, Grey Level Co-occurrence Matrix. ABSTRACT A new procedure for multispectral image classification was investigated in this study. This procedure incorporates textural features into conventional per-pixel classification and is implemented with artificial neural networks. The grey level co-occurrence matrix for textural measure calculation is derived from a supervised spectral classification based on three SPOT spectral bands (XS1, XS2 and XS3). Four textural measures, contrast, entropy, angular second moment and inverse difference moment, are calculated based on the grey level co-occurrence matrix. An artificial neural network using a variant of the back-propagation algorithm is applied to the procedure. The performance of the new procedure is assessed relative to that of a classification combining spectral features and textural measures based on the grey level co-occurrence matrix from the near-infrared SPOT band and a per-pixel classification procedure. The test results show that the classification accuracy with the new procedure can be improved by more than 10%, compared to the other two classification procedures. The study indicates that the textural measures based on the grey level co-occurrence matrix from the supervised spectral classification can reveal more effectively spatial forms of landcover and landuse in multispectral images. However, an inappropriate window size for textural feature extraction affects the fidelity of textural features. Window sizes of 5x5 and 7x7 can be considered as the appropriate ones for landcover and landuse classification in this test area. 1. INTRODUCTION With the advent of higher spatial resolution satellite images and their wide application to landcover and landuse mapping, conventional per-pixel classifiers present more and more limitations for the data processing. This is mainly due to two factors. Firstly, conventional classifiers assume that landcover classes in satellite images follow some kind of specific distributions, e.g. Gaussian distribution, but in fact they are often spectrally complex and heterogeneous. Secondly, per- pixel classifiers are based only on spectral features, ignoring the spatial information in images. Many efforts have already been devoted to overcome these problems, and three procedures are representative among them. The first incorporates textural features into image classification (Haralick et al., 1973; Jensen, 1979; Marceau et al., 1990; Baraldi and Parmigianni, 1990; Sadler et al., 1991). However, this method is under dispute. Some studies indicated that it only provides a relatively small and sometimes even no practical improvement of the accuracy of landcover and landuse classification in urban areas compared to the use of only spectral features (Jensen, 1979; Baraldi and Parmigianni, 1990; Sadler et al., 1991). There are two possible reasons for these results: (a) the procedure used for extraction of textural features is based only on a single spectral image; thus, textural measures cannot completely reveal spatial forms of landcover and landuse in multispectral images; (b) the method also depends on statistical assumptions about class distributions. The second procedure is a spatial (or contextual) reclassification technique (Wharton, 1982; Gong and Howarth, 1992; Fung and Chan, 1994). As two subsequent classifications are implemented in this scheme, errors from the two stages and their interaction would probably result in a decrease in the overall classification accuracy. The third procedure applies artificial neural networks (ANNs) in multispectral classification (Bischof et al., 1992; Paola and Schowengerdt, 1997). A continuously increasing number of investigations is focused on this classification procedure for three reasons: - The procedure does not require any a priori knowledge of the statistical distribution of the data, i.e., nonparametric classification. - It can classify landcover and landuse classes with any distribution, i.e. not only Gaussian but also multimodal or even disjoint. This means that the procedure is capable of classifying classes with heterogeneous data and high spatial variability. - It is adaptable and robust. Although this procedure results in high classification accuracy, typically more than 80%, it is also necessary to combine textural features into the procedure to fully exploit the potential of ANNs. However, very few attempts have been made up to now in this direction.

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