Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Detection of subpixel woody features in simulated SPOT imagery. Patricia G. Foschi

Document type:: Multivolume work

Structure type:: Chapter

narrow or absent. Real SPOT imagery of the United Kingdom would be recorded in the morning and would contain larger shadows, which are expected to be use ful in detecting hedgerows. 2.2 Preprocessing of simulated imagery The preprocessing of the simulated imagery included two image rectification techniques applied to the raw data recorded by the Daedalus scanner (Hunting Geology and Geophysics Ltd. 1984). One was a linear scaling technique by which the swath was scaled to fit a map. The second technique was an S-bend correction which rectified distortions caused by the radial rotation of the scan mirror. This latter correction is unnecessary in data from push-broom scanners like those on board the SPOT-1 satellite. During the course of the project, it was discovered that the three simulated multispectral bands were not properly registered to one another. Whether this prob lem is due to preprocessing or subsequent processing is presently unknown. The misregistration was verified by two methods! 1. By locating the edges of obvious features in printed arrays of DN values and comparing the loca tions in each spectral band. 2. By visually comparing registered and unregistered colour-composite images on adjacent display terminals. This misregistration was corrected by shifting band 1 one pixel to the west and by shifting band 3 one pixel to the east. 3 PREVIOUS METHODS FOR CLASSIFYING MIXED PIXELS Figure 1. Classification zones in a hypothetical feature space. training set mixed pixel region training set In the literature, there are two types of methods for classifying mixed pixels. In the first type, mixed pixels are treated as whole entities and are assigned to a single class. It is assumed that the class as signment corresponds to the dominant constituent class. Textural (Haralick 1979) or contextual informa tion (Gurney & Townshend 1983) may be incorporated in to the classification procedure. The second type of classification method for han dling mixed pixels involves pixel splitting, the proc ess of breaking a pixel into its component parts and classifying the fractions. Pixel splitting methods are based on the premise that the gray tone represented by a digital number of a pixel is proportional to the gray tones of the constituent classes. Four statisti cal procedures for pixel splitting have been found in the literature! weighted averaging (Marsh et al. 1980), linear regression (Nalepka et al. 1972, Richardson & Weigand 1977), maximum likelihood classification (Chittineni 1981, Horwitz et al. 1971), and linear discriminant analysis (Marsh et al. 1980). The first three of these procedures have been or could easily be used in the three-class case, the usual condition for pixels containing narrow woody features. For a number of reasons, the methods already devel oped seemed inappropriate for this project. Treating mixed pixels as whole entities was not appropriate be cause linear woody features are seldom the dominant constituent class. The variances of the major classes in the subscene are significantly different; the var iance of woody vegetation is significantly larger than the variance of any other class. Therefore, it seemed unsuitable to use either weighted averaging which does not take variance into consideration or linear dis criminant analysis which requires the variances of all classes to be equal. The linear regression method was eliminated since development of the model requires specific information regarding the proportions of com ponent parts within mixed pixels. The small sizes of the features of interest and the geometric distortions in the image make collection of this specific informa tion difficult. Finally, the maximum likelihood method seemed inappropriate because it is the most computa tionally demanding of the known methods. Since various postprocessing algorithms are planned, it seemed de sirable to conserve computer time. Figure 2. Three parts of the 13 x 3 window. 4 NEW METHOD FOR CLASSIFYING SUBPIXEL WOODY FEATURES The classification method developed for this project discriminates selected subdivisions of feature space. Figure 1 illustrates the subdivisions of interest in a hypothetical feature space which was constructed us ing two spectral channels and five pixels from each of three classes. If the triangles and squares represent two crops or any two land covers present in adjacent fields, pixels containing the boundary between the fields are expected to fall between the line joining points B and C and the line joining points E and F. If, however, the boundary between the fields includes a linear woody feature and if the circles represent woody vegetation, pixels containing this boundary are more likely to fall within the polygon circumscribed by points A, B, G, and D. Locating a pixel in one of these two zones is the basis for classifying it in ei ther the class of normal boundaries or the class of small woody features. Nine major classes present in the study site were plotted in the two-dimensional feature spaces whose axes are pairs of the multispectral bands. The pan chromatic data was not used. Visual inspection of the plots revealed that bands 1 and 2 are highly correlat ed and that the greatest separation between classes occurs when bands 2 and 3 are used. Therefore, only bands 2 and 3 were included in subsequent processing; band 1 was eliminated due to its redundancy. A training set for woody vegetation, consisting of 80 pixels, was selected from a large patch of decidu ous woodland in the subscene and was used in all fea ture-space calculations. A 13 x 3 window was employed to locate its central pixels in the two-channel feature space. As shown in Figure 2, the window was divided into three parts: two training sets for the adjacent fields and a central region expected to contain mixed pixels. These two training sets and the training set for woody vegeta tion were used to construct a feature space for each window. The central pixels in the window were then Figure 3* Fea-t classified by ■ Examples of Figures 3-5. I vegetation, tr pointing upwar rived from the tral pixels in dicates the pi L and S refer the centre. In of or below th woody features 5 RESULTS AND : Twenty subpixe! boundaries werf initial testing greatly vary ir rows, a hedge \ trees, and stri All of the wooc incorrectly del classification. to be either de visual inspects The classifie correctly class er than hedgero field boundarie the hedges. Fur Use of the pa into future cla improve classif Figures 3-5 1 training sets m roughly triangu sets in Figure ; When adjacent f; the clusters of ure 4. In both < woody features ; woodland cluster clusters are po: Figure 5» pixel: less likely to 1 the zone design: only occurred or Before this me

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