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 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: ADAPTIVE FUSION OF MULTISOURCE RASTER DATA APPLYING FILTER TECHNIQUES. K. Steinnocher

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 figure shows an (idealized) output of the fusion process, where the shape of the objects is identical with the panchromatic image, but the spectral behavior of the objects corresponds to the multispectral image. Fig. 1. Effect of Adaptive Image Fusion: (a) panchromatic image ; (b) multispectral image ; (c) fused multispectral image. Although AIF sharpens the multispectral image according to object edges found in the panchromatic image, this effect is limited to object edges that occur both in the panchromatic and the multispectral image. Edges appearing only in the panchro matic image will cause no significant effect in the multispectral band, while edges in the multispectral band that do not show up in the panchromatic image will get slightly blurred. Objects that are smaller than the original multispectral pixel size will only be sharpened, if their spectral reflectance is significantly different from their local environment. At the same time as object edges are sharpened, the area within an object will be smoothed. This effect can be seen as a pre-segmentation of the multispectral image. It reduces the occurrence of mixed pixels, thus improving the quality of a subsequent classification. 3. APPLICATION 3.1. Agricultural case study The AIF algorithm was first applied to a multisensor image acquired by the Indian remote sensing satellite IRS-1C. This system provides panchromatic images with a spatial resolution of 5.6m (PAN), and three multispectral bands (green, red and near infrared) with a resolution of 23.5m (LISS-2/3/4). The commercially available scenes are radiometrically preprocessed, panorama corrected and resampled to a pixel size of 5m and 25m respectively. The acquired scene (path 30, row 35, quadrant A) was sensed on August 9, 1996 and covers an area of approximately 70x70 km 2 located in Upper Austria (centered on 13°35’E / 47°58’N). The images were geocoded to the Austrian reference system (GauB-Kruger M31). The multispectral bands were resampled to 5m pixel size to match the resolution of the panchromatic image. Application of AIF requires the selection of two parameters, the normalised standard deviation and the size of the local window. The former can be estimated from the panchromatic image as described in section 2.1 The minimum size of the local window is the ratio between original multispectral and panchromatic pixel size. This minimum size offers the advantage of short computation time. However, test runs of AIF have shown that larger window sizes, such as 9x9 or 11x11, lead to significantly better results. This will increase the computation time of the program, but at the same time will reduce the number of iterations. This is due to the stronger effect of averaging, when larger window sizes are used. For the demonstration study a normalized standard deviation of 0.01 and a window size of 11x11 were chosen. Each multispectral band was fused subsequently with the panchromatic image, thus leading to a synthetic multispectral image stack. Being the most commonly used merging technique, an IHS procedure was performed for comparison. Visual evaluation of the fusion results shows the differences between AIF and IHS merging (Fig. 2). The impression of the AIF image is that of a pre-segmented image, where the variation of grey levels within the objects is very low, while the objects are clearly separated from each other. Areas with a high variance in the panchromatic image, such as the village left of the center, appear like one homogenous object in the fused image. The IHS image has more textural information that supports visual interpretation, but also distorts the spectral information significantly, as can be seen in some of the agricultural fields. Multispectral classification of the fused images confirms the different effects (Fig. 3). A comparison of the classification of the original image and the AIF image, both classified with the same set of spectral signatures, shows the similar pattern of assigned classes. The delineation of objects is significantly better in the AIF image, and it contains less single pixel objects. Classification of the IHS image was performed with a different set of spectral signatures, as the spectral distortions did not allow using the signatures from the original multispectral image. The appearance of the classification is noisier, and in some areas differs significantly from the original classification. Quality assessment. The assessment focuses on how much the radiometry of the multispectral images is distorted by the fusion procedure. It is based on the idea that a synthetic image once degraded to its original resolution should be as identical as possible to the original image. This property is estimated by comparing the mean values and standard deviations of and the correlation coefficients between the degraded and the original images (Wald et al., 1997). In order to produce the degraded images, the fusion results were averaged applying a 5x5 filter kernel and resampled to 25m pixel size. Table 1 presents the results of the comparison between the degraded images and the original data. The first column shows the global mean (p) and standard deviation (a) of the original multispectral bands. The columns entitled AIF and IHS give the respective values of the merged products. The results of AIF show no significant differences in the mean values, but the standard deviation is slightly reduced. This is due to the averaging performed during the merging process. IHS merged images show a higher deviation in the mean values and an increase of the standard deviation resulting from the inclusion of textural information from the panchromatic image. It is interesting to note, that for channel 4 (NIR) the standard deviation has decreased during the IHS merge.

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