Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

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:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: DETECTING TEXTURE EDGES FROM IMAGES. HE Dong-chen and WANG Li

Document type:: Multivolume work

Structure type:: Chapter

In a square raster digital image, each pixel is surrounded by eight neighbouring pixels. The local texture information for a pixel can be extracted from a neighbourhood of 3 x 3 pixels, which represents the smallest complete unit (in the sense of having eight directions surrounding the pixel). Given a neighbourhood of 3x3 pixels, which will be denoted by a set containing nine elements: V= {Vo, Vi, ..., V8), where Vo represents the intensity value of the central pixel and Vi, {i=l, 2, ..., 8}, is the intensity value of the neighbouring pixel i, we define the corresponding texture unit by a set containing eight elements, TU={Ei, E2, ..., Es}, where Ei, (i=l, 2,..., 8) is determined by the formula: { 0 if Vi<Vo 1 if V i= V 0 for: i=l,2,..., 8 2 if Vi>Vo and the element Ei occupies the same position as the pixel i. As each element of TU has one of three possible values, the combination of all the eight elements results in 3 8 =6561 possible texture units in total. There is no unique way to label and order the 6561 texture units. In our study, the 6561 texture units are labeled by using the following formula: 8 NTU = ^ Ei* 3 1 - 1 i=l where Ntu represents the Texture Unit Number and Ei is the ith element of texture unit set TU={Ei, E2,..., Es}. The previously defined set of 6561 texture units describes the local texture aspect of a given pixel, that is, the relative grey level relationships between the central pixel and its neighbours. Thus, the statistics on frequency of occurrence of all the texture units over a large region of an image should reveal texture information. We termed texture spectrum the frequency distribution of all the texture units, with the abscissa indicating the texture unit number Ntu and the ordinate representing its occurrence frequency. It should be noted that the labeling method chosen may affect the relative positions of texture units in the texture spectrum, but will not change their frequency values in the texture spectrum. II II should be also noted that the local texture for a given pixel is characterized by the corresponding texture unit, while the texture aspect for an uniform texture image is revealed by its texture spectrum calculated within an appropriate window. The size of the window depends on the nature of the texture image. 3 TEXTURAL EDGE DETECTION The principal idea of textural edge detection is to use texture spectrum as the texture feature and to combine it with traditional operators. That is, when applying a traditional edge detection operator over an image, the grey level of each element of the operator will be replaced by the texture spectrum calculated from the corresponding neighbourhood. Some texture images have been choosen to evaluate the method. They are illustrated in Figures 1 and 2. These images are represented by 512x512 pixels with 64 normalized grey levels and are composed of six different textured areas. These six texture images are extracted from Brodatz' album (Brodatz, 1968). They are respectively the images D4: pressed cork; D24: pressed calf leather; D29: beach sand; D38: water; D57: handmade paper and D93: fur hide of unborn calf. These natural texture images have been chosen because they are broadly similar to one another, and are similar to parts of digital images usually encountered in practice, for example, landscape scenes provided by earth observation satellites. The six textures of Figure 1 constitute one vertical boundary and four diagonal ones, while Figure 2 presents four circles with two straight lines (horizontal and vertical). This may represent most of the complex situations encountered in natural images. In our study, the Roberts operator was used as the edge detection operator. Texture spectra were calculated using a moving window of 30 x 30 pixels. The integrated absolute difference between two texture spectra has been taken as the difference between two elements of the edge detection operator: R(oberts) = V d] + d 2 2 6561 Dl= I Si J(k) “Si+l,j+l(k)l k=l D2 = 6 ^ISij + i(k)-Si + i,j(k)l k=l where: Sij(k) denotes the kth element of the texture spectrum calculated from the window located at the position (i, j). Thus, Di and D2 give the absolute difference between two texture spectra located in diagonal positions. The convolution using the above operator was carried out over the whole images of Figures 1 and 2 with a step of 1 in line and column. The results are illustrated respectively in Figures 3 and 4, where the grey levels of the images are linearly stretched from 0 to 250.

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