Topographic Normalization Lab

8/2/2019 Topographic Normalization Lab

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TOPOGRAPHIC NORMALIZATION USING HYPERION IMAGE

Mohamad Izwan bin Ghazali

3 SGS

1. Introduction

High spatial resolution remote sensing images in rugged areas often havegreat terrain effects, so besides atmospheric correction, topographic correction isalso a necessary step of pre-process in the remote sensing application for rugged

areas. developing topographic correction technique of remote sensing imagery hasgreat importance in forestry survey, resources exploration and other interrelatedfields. So far, the scientists in the field of remote sensing have developed manymethods of topographic correction, such as cosine correction, C correction, Minaretscorrection (Smith, 1980; Teillet, 1982, 1986; Colby, 1991; Meyer, 1993 et al.). Thethree methods are STS (sun-surface-sensor) methods, which have an assumption(Scott, 2005) that a pixel in rugged terrain can be considered as a declining plane tohandle, and all the illumination correction is based on the assumption.

2. Objective

To perform topographic normalization prior to remotely sensed image classification

3. Data

This lab exercise will use the following image/ancillary data:

a. Hyperion Hyperspectral Image over Pasoh Forest Researchb. Topography Map of Pasoh Forest Reserve

Image Processing Software : ERDAS 9.2

AUTOCAD map 2012

ENVI 4.5


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4. Study Area

Study area is area Pasoh, Negeri Sembilan. Hyperion image that being used in thisstudy. This image already done with geometric correction

Figure 1. Hyperion Image

5. Methodology

The methodology used in study is as shown in figure 2

Figure 2. Flow chart of Methodology

Topographic Normalize

Compare and combine Hyperion image with DEM

Digital Elevation Model

Develop DEM using ERDAS software

Digitizing

Using topographic map to create contour using AutoCad


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6. Digital Elevation Model

Digital Elevation Models are data files that contain the elevation of the terrain

over a specified area, usually at a fixed grid interval over the "Bare Earth". Theintervals between each of the grid points will always be referenced to somegeographical coordinate system. This is usually either latitude-longitude or UTM(Universal Transverse Mercator) coordinate systems. The closer together the gridpoints are located, the more detailed the information will be in the file. The details ofthe peaks and valleys in the terrain will be better modeled with small grid spacingthan when the grid intervals are very large. Elevations other than at the specific gridpoint locations are not contained in the file. As a result peak points and valley pointsnot coincident with the grid will not be recorded in the file.

Quality DEM products are measured by how accurate the elevation is at eachpixel and how accurately the morphology is presented. Several factors are importantfor quality of DEM-derived products:

a. Terrain roughnessb. Sampling density (elevation data collection method)c. Grid resolution or pixel sized. Interpolation algorithme. Vertical resolutionf. Terrain analysis algorithm

Common uses of DEMs include:

a. Extracting terrain parametersb. Modeling water flow or mass movement (for example, landslides)c. Creation of relief mapsd. Rendering of 3D visualizationse. Creation of physical models (including raised-relief maps)f. Rectification of aerial photography or satellite imageryg. Reduction (terrain correction) of gravity measurements (gravimetry, physical

geodesy)h. Terrain analyses in geomorphology and physical geography

7. Topographic Normalization

The correction of illumination variations is referred to as TopographicNormalization or Topographic Correction. Techniques are grouped into two majorcategories, (1) band ratios and (2) modeling of illumination conditions. Band ratiotechniques assume that the spectral response is distorted (increased or decreased)in the same way across all bands. Therefore, the quotient between them will

compensate for topographic effects. Their drawback is the loss of spectral resolution.Techniques under group (2) model illumination to compute the flat-normalized


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radiance of each pixel. They are grouped into two additional subcategories,Lambertian and non-Lambertian, depending on whether they assume a Lambertianor nonlambertian surface behavior.

A Lambertian surface is presumed to be a perfectly diffuse reflector, appearing

equally bright from all viewing directions (Ekstrand, 1996). Therefore a Lambertiansurface is presumed to be a perfectly diffuse reflector, appearing equally brightfrom all viewing directions (Ekstrand, 1996). Therefore, a Lambertian correctionfunction attempts to correct only for differences in illumination caused by theorientation of the surface (Jones et al., 1988).The incidence angle (i) is the anglebetween the surface normal and the solar beam, and may be calculated atequation 1 (Smith et al., 1980). When the sun is not zenith, correction of theradiance of a projected horizontal surface would be achieved by the equation.


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8. Result and analysis

Contour DEM (Digital Elevation Model)


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Topograpic Nomalization Classification Unsupervised (K-Mean)

Hyperion Image Topographic nomalization

Using topograpic normalization (lambertian model) shown more accurate classificationbefore topograpic normalization.


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9. Conclusion

After topograpic normalization, combination between DEM and classification,we can make land cover map more accurate because this technique can cover hill

area because when satellite take some image not covered by sun light, so DN canappear dark on image. Using this technique can predict DN for lost data on image

Reference

Comparison of Lambertian and non-Lambertian topographic normalizationalgorithms: A case study in Gelibolu, Turkey , Mufit Cetin

A Model Of Topographic Correction And Reflectance Retrieval For OpticalSatellite Data In Forested Areas, Cheng Wei, Tian Qingjiu, Wang Liming

Influence of different topographic correction strategieson mountain vegetationclassification accuracy in the Lancang Watershed, China. Zhiming Zhang,Robert De Wulf,Lieven P. C. Verbeke,Frieke M. B. Van Coillie,Eva M. DeClercq,and Xiaokun Ou

Topographic Normalization Lab

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