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 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: TOOLS AND METHODS FOR FUSION OF IMAGES OF DIFFERENT SPATIAL RESOLUTION. C. Pohl

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 the input images to link related parameters of the observed Earth surface. c Fig. 1. Overall data fusion process in remote sensing. After having corrected system-induced and geometric errors in the dataset as indicated in Fig. 1, the images are fused to produce the higher spatial resolution using one of the following techniques: - RGB colour composites; Intensity Hue Saturation (IHS) transformation; Arithmetic combinations (e.g. Brovey transform); Principal Component Analysis; Wavelets (e.g. ARSIS method); - Regression Variable Substitution; - Combinations of techniques. The following sections describe the context and process of various techniques in more detail. 3.1. Red-Green-Blue Colour Composites The so-called additive primary colours allow the assignment of three different types of information (e.g. image channels) to the three primary RGB colours. Together they form a colour composite that can be displayed on conventional media, e.g. cathode ray tube (CRT), with the parallel use of a LookUp- Table (LUT). The colour composite facilitates the interpretation of multi-channel image data due to the variations in colours based on the values in the single channels. Operations on the LUT and the histogram of the image data can enhance the colour composite for visual interpretation. The possibilities of varying the composite are manifold. Depending on the selection of the input image channels, the fused data will show different features. Very important for the colour composite is the distribution of the available 0-255 grey values to the range of the data. It might be of advantage to invert input channels before combining them in the RGB display with other data depending on the objects of interest to be highlighted (Wang et al., 1995). the colour aspects in its average brightness representing the surface intensity, its dominant wavelength (hue) and its purity (saturation) (Gillespie et al., 1986; Carper et al., 1990). The IHS values, commonly expressed in cylindrical or spherical coordinates, can be mapped to Cartesian coordinates through values v lt v 2 using a linear transformation (Harrison and Jupp, 1990): f 1 i l i fI Vi Vs Vi 1 1 2 V 1 T* Te G V2> i l [Tl ~Ti 0 H = tan~'(—) (b) S = V v 2 +V, 2 V ! f 1 1 1 N (R\ Vi Tb S fl G 1 1 1 * v V3 V6 1 2 Te 0 J (1) (2) In order to apply this technique for the enhancement of spatial resolution, a panchromatic higher resolution channel replaces the intensity component of a lower resolution multispectral dataset. There are two ways of applying the IHS technique in image fusion: direct and substitutional. The first refers to the transformation of three image channels assigned to I, H and S. The second transforms three channels of the dataset representing RGB into the IHS colour space which separates the colour aspects from its average brightness (intensity). Then, one of the components (usually intensity) is replaced by a fourth higher spatial resolution image channel, which is to be integrated. In many published studies the channel that replaces one of the IHS components is contrast stretched to match the latter. A reverse transformation from IHS to RGB as presented in Eq. 2 (Harrison and Jupp, 1990) converts the data into its original image space to obtain the fused image. 3.3. Arithmetic Combinations The possibilities of combining the data using multiplication, ratios, summation or subtraction are manifold. The choice of weighing and scaling factors may improve the resulting images. Eq. 3 gives an example of a summation, and Eq. 4 of a multiplication technique used to combine Landsat TM with SPOT PAN as resolution merge (Yesou et al., 1993). DN f = A(w, * DN a + w 2 * DN b ) + B (3) DN f = A* DN a * DN b + B (4) 3.2. Intensity-Hue-Saturation Colour Transform The IHS transformation (Eq. la-c) separates spatial (I) and spectral (H, S) information from a standard RGB image. It relates to the human colour perception parameters. It separates A and B are scaling and additive factors respectively and Wi and w 2 weighting parameters. DN f , DN a and DN b refer to digital numbers of the final fused image and the input images a and b, respectively.

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