Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB)

Transcription

1 Asia Link Project FORRSA Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Uwe Ballhorn GeoBio-Center Ludwig-Maximilians-Universität, Munich, Germany & RSS - Remote Sensing Solutions GmbH, Munich, Germany Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

2 Content 1. Image Processing Workflow 2. What is Prep-Processing? 3. Types of Pre-Processing 3.1. Geometric Correction 3.2. Radiometric Correction 3.3. Noise Removal 3.4. Georeferencing 4. Results of Pre-Processing Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

3 1. Image Processing Workflow A. Pre-Processing: eliminate data registration errors geometric correction: earth rotation earth curvature instability of the platform (altitude, velocity, pitch, roll and yaw) topographic effects radiometric correction noise removal georeferencing Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

4 1. Image Processing Workflow B. Image Enhancement: change the visual impression visualisation RGB display / MCGB printer output false colour image concept contrast stretching linear/ non linear histogramm manipulations filter operations : change intensity according to surrounding pixel Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

5 1. Image Processing Workflow C. Image Analysis principal component calculation indices calculations ratios f.e. simple ratio = band 1/ band 2 vegetation indices like NDVI = band 1 band 2 / band 1 + band 2 multivariate data analysis (traditional) supervised classification unsupervised classification object oriented image analysis D. Accuraccy Assessment Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

6 2. What is Pre-Processing? correct distorted or degraded data create a more faithful representation of the original scene typically involves the initial processing of raw image data to correct for: geometric distortions calibrate the data radiometrically eliminate noise present in the data image restoration process highly dependent upon the characteristics of the sensor used normally precede further manipulation and analysis of the image data to extract specific information Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

7 3. Types of Pre-Processing 3.1. Geometric Correction 3.2. Radiometric Correction 3.3. Noise Removal 3.4. Georeferencing Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

8 3.1. Geometric Correction raw digital images usually contain geometric distortions so that they cannot be used directly as a map base without subsequent processing sources of geometric distortions: variations in the altitude, attitude, and velocity of the sensor platform panoramic distortion earth rotation earth curvature atmospheric refraction relief displacement nonlinearities in the sweep of a sensor s IFOV etc. Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

9 3.1. Geometric Correction Earth Curvature Earth Rotation Effect of earth curvature on the size of a pixel in the scan direction The effect of earth rotation on scanner imagery: a. Image formed by lines arranged in a square grid b. Offset of successive lines to the west to correct for the rotation of earth s surface during the frame acquisition time Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

10 3.1. Geometric Correction Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

11 3.2. Radiometric Correction type of radiometric correction varies widely among sensors sources of radiometric distortions: changes in scene illumination atmospheric conditions viewing geometry instrument response characteristics need to perform correction for any or all of these influences depends directly upon the particular application at hand Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

12 3.2. Radiometric Correction Examples Sun elevation correction: accounts for the seasonal position of the sun relative to the earth image data acquired under different illumination angles are normalized by calculating pixel brightness values assuming the sun was at the zenith on each date of sensing Earth-sun distance correction: applied to normalize for the seasonal changes in the distance between the earth and the sun Haze compensation: procedures designed to minimize the influence of path radiance effects Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

13 3.3. Noise Removal unwanted disturbance in image data due to limitations in the sensing, signal digitization, or data recording process sources of noise: periodic drift or malfuction of a detector electronic interference between sensor components intermitted hiccups in the data transmission and recording sequence etc. can either degrade or totolly mask the true radiometric information content noise removal usually precedes any subsequent enhancement or classifiaction process objective is to restore an image to as close to the original scene as possible Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

14 3.3. Noise Removal Result of applying noise reduction algorithm: a: original image data with noise induced salt and pepper appearance b: image resulting from application of the filter algorithm in (c) (from Lillesand and Kiefer, 1999) Typical noise correction algorithm employing a 3x3 neighbourhood. WEIGHT is an analyst specified weighting factor. The lower the weight, the greater the number of pixels considered Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

15 3.4. Georeferencing another important domain in pre-processing is georeferencing geographic location is the element that distinguishes geographic information from all other types methods for specifying location on the earth s surface are essential to the creation of useful geographic information georeferenced images contain information concerning spatial location and pixel size, so that statements regarding distance and area can be given often image data is not correctly georeferenced with the help of different data the spatial location can be addressed to image data Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

16 3.4. Georeferencing Goal of the exercise Georeferencing of a Landsat scene which has no information concerning spatial location. In this exercise another scene from the same area is used as base for the georeferencing process. Especially for change detection in vegetation cover it is important that two satellite scenes fit exactly over each other. So this is an important step in pre-processing remote sensing data. Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia

17 4. Results of Pre-Processing Data representing physical radiation measurements (radiometric calibration) Data fitting into the geographic reference system chosen as base for the investigation or the administrative GIS data holding environment (geometric rectification) Data base for image analysis Pre-Processing of Remote Sensing Data Bogor Agricultural University (IPB) Indonesia