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 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION. Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin

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 objects, such as buildings, do not coincide on the different images (e.g., parallel green, yellow and red edges in the upper row of buildings). This suggests that the Instantaneous Field of View of the sensor had a significant spectral dependence. Unsupervised classification. In solving remote-sensing problems, classification - sometimes combined with contextual information - is usually expected to provide the final answer. To increase the reliability and robustness of classification, many researchers favor supervised techniques. In our object recognition scheme, classification is just one of the early vision processes that provides only partial, incomplete information for object recognition. Since the number of classes is usually not known a priori and no training data is available, we employ unsupervised classification methods. Unsupervised classification explores the inherent cluster structure of the feature vectors in the multidimensional feature space. Clustering usually results in a grouping, where the variance within a cluster is minimized, while maximizing it between the clusters. Clusters are not intrinsic properties of the set of features under consideration. There is a risk that, instead of finding a natural data structure, we would be imposing an arbitrary or artificial structure, for example, by selecting an unreasonable number of clusters. Therefore, it is inevitable to analyze the distribution of the classes and their separability in feature space. In this test, we merged the visible-NIR bands (3-10) of the multispectral scanner data by using the well-known ISODATA methods. At the heart of the ISODATA scheme is an updating loop that, using a distance measure, reassigns points to the nearest cluster center, each time the center is moved (Nadler and Smith, 1993). Since the number of different cover-types is scene dependent and usually not known a priori, the dataset was classified several times with increasing the number of classes each time. Because some of the spectral bands are highly correlated, different band combinations were additionally tested. Each classification was compared with the ground truth. Additionally, the separability of classes was analyzed. Different separability measures are described in the literature. To find the best definition is not a trivial task (Schowengerdt, 1997). For our clusterings, the different separability measures (Mahalanobis, divergence, Jeffries-Matusita, etc.) provided very similar results. We obtained the best clustering results, when using the complete 8-band dataset (see Fig. 3b, 3c). However, when using only 4 bands, selected from different spectral positions, still acceptable results were obtained. Six major cover types were distinguished in the scene (Figure 3b), namely water and roof (black, 1), roof (dark green, 2), vegetation (red, 3 and 4), and roof and bare soil (light gray and white, 5 and 6). Using more classes, for example ten (Figure 3c), some of the classes were split, giving rise to new classes with relatively low separability. Comparing the cluster maps with the aerial photographs reveals that despite the confusion between water and roof pixels, and bare soil and roof pixels, the boundary between man-made surfaces (buildings, walkways, driveways, roads) and vegetated natural surfaces is always recognizable. Note that other boundaries, such as the ones between bare soil and grass, and between vigorous and sparse vegetation, are also present, even though these boundaries are not related to any objects of interest. It is very important to emphasize that no building or roof spectra exist, as it is well known from previous studies. For example, the 6-class clustering classified roof pixels into four different classes with distinctly different spectra throughout the entire range. To include information about the quality of the clustering in the visual representation, we introduce the concept of weak and strong boundaries. Weak boundaries are located between pixels belonging to classes with low separability; they are of secondary importance. In the 6-class clustering, all boundaries are strong. However, the 10-band clustering rendered 3 weak boundaries, from a total of 45. The use of weak and strong boundaries helps considerably in organizing and simplifying edges. Fig. 2 a. Visible image and detected edges; b. NIR image and detected edges; c.Thermal image and detected edges.

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