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 A new classification procedure is investigated in this study, combining spectral and textural features for multispectral image classification using ANNs. It consists of two stages. The first stage involves spatial information extraction. A supervised spectral classification of classes with unimodal distribution using ANN was used. Classes with unimodal spectral distribution can be obtained in one of the following three cases: (a) a single landcover or landuse; (b) a unimodal distribution part (subclass) of a multimodal distribution landcover or landuse; (c) a mixture part of different landcover and landuse classes with similar spectral distribution, or mixed pixels. Then, the classification result is used to derive a grey level co occurrence matrix for textural feature extraction. Four textural measures, i.e. contrast, entropy, angular second moment and inverse difference moment, were calculated based on the grey level co-occurrence matrix. The second stage involves combining the spectral and textural images for landcover and landuse classification. An ANN with a variant of the back- propagation algorithm was used in both stages. This study tries to answer the following questions through the new classification procedure: (1) Can the classification accuracy be improved significantly by incorporating textural features into per-pixel classification procedures? (2) Does the fidelity of textural features depend on the grey level co-occurrence matrix? (3) What is the influence of the window size for textural feature extraction on the classification accuracy? 2. METHODOLOGY In order to answer the above questions, the following experiments were performed and their results evaluated: Production of textural indices according to an existing technique proposed by Haralick (Haralick et al., 1973). Production of textural indices based on the proposed new technique using multiple spectral bands. ANN supervised classifications of 9 landcover/landuse categories based respectively on original SPOT XS bands and a combination of spectral bands and two procedures for textural indices calculation with four window sizes each. The evaluation phase will then compare the nine classification results. 2.1. Data Description Experiments and evaluations were performed on a portion of a SPOT XS image (20m resolution) acquired on July 20, 1990 around Geneva, Switzerland. This study area contains 512 x 512 pixels (about 100 km 2 ). An aerial image of the same area with a ground resolution of 2.5m x 2.5m and acquired on July 24, 1992 was used as ground truth. Nine landcover and landuse categories (Table 1) dominate the study area. Class Abbreviation Crop 1 CR1 Crop 2 CR2 Trees T Grass G Parks P Apartment-block areas ABA Low density urban areas LDUA High density urban areas HDUA Water W Table 1. Landcover and landuse categories in the study area. 2.2. Textural Indices Extraction Two procedures to derive the grey-level co-occurrence matrix for textural indices calculation were considered at this stage. The first procedure is based on a scheme that reduces the grey values of the near-infrared band (XS3) to 18 levels with equal- probability of occurrence (Haralick et al., 1973). Then, this image with 18 levels was used to calculate the grey-level co occurrence matrix (termed matrix 1) for textural measure extraction. The near-infrared band was chosen because it exhibits better contrast between different landcover and landuse classes than the other two bands (Marceau et al., 1990). The number of levels was set to 18 in order to permit comparison with the second procedure, although other number of levels can be used. The second procedure is the new one proposed here. It is based on the supervised spectral classification result, which was considered as another grey-level co-occurrence matrix (termed matrix 2) for textural measure extraction. This supervised spectral classification was performed by an ANN based on three bands (XS1, XS2 and XS3). Spectral categories in the classification are required to have unimodal spectral distribution. Thus, eighteen spectral categories with unimodal spectral component were obtained in this study area. They are: large buildings, part of apartment-block areas, part of high density urban areas, part of low density urban areas, crop 1, part of crop 2, fallow, bare soil, trees, grass 1, grass 2, part of parks, turbid water, clear water, mixed class 1, mixed class 2, mixed class 3 and mixed class 4. Training data for the spectral classification were chosen based on a composite image of three SPOT bands, the aerial image, and a scattergram of the red band (0.61-0.68 (im) and near- infrared band (0.79-0.89 (im) of the SPOT images. A total of 1620 pixels were selected as training data. 720 pixels, which were non-neighbouring and independent of the training data, were chosen as test data. Spectral categories in the classification result were given an ordinal value based on their spectral mean value derived from the training data. The spectral class with the smallest spectral mean in the training data was given the value 1, the one with the second smallest spectral mean, the value 2 and so on, up to the value 18 for the class with the largest spectral mean. 176

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