QGis Image Classification Tutorial: 2.3- The spatial filters

The spatial filters represent another method of digital processing used for the enhancement of an image. These filters are designed to bring out or remove specific features of an image based on their spatial frequency. The spatial frequency is related to the concept of texture. It refers to the frequency of variation of the different tones that appear in an image. The regions of an image where the texture is “rough” are the regions where the changes in different shades are steep; these regions have a high Spatial frequency. The “smooth”regions have a variation of tones that is more gradual over several pixels;these regions have a weak Spatial frequency Space. The spatial filtering method implies moving a “window” the size of a few pixels (eg. 3 of 3, 5 out of 5,etc.) above each image pixel. Then, we apply a mathematical processing using the values of the pixels under the window and replace the value of the central pixel by the result obtained. The window is moved along the columns and lines of the image, one pixel at a time, repeating the calculation until the whole image has been filtered. By modifying the calculation performed within the window, is possible to enhance or remove different types of features present in an image.

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ArcMap Image Classification Tutorial: 2.5- Exploring Data

Different multi spectral data bands have, often, a very high correlation and contain similar information. For example, the sensors of Landsat MSS bands 4and 5 (green and red respectively) produce visual images with very similar appearance being given that the reflectance for the same type of surface is almost identical. Image transformations based on complex statistical multi spectral data processing can be used to reduce data redundancy and the correlation between the bands. The analysis of the main components is a transformation of this type. The goal of this transformation is to reduce the number of dimensions (number of bands) and to produce a compression of information from several bands in a smaller number of bands. The ”   new   ” bands that result from this statistical compression are called components. This process aims to maximize (statistically) the quantity information (or variance) of the original data in a restricted number of components. For example, the analysis of the main components, can transform data from seven bands of the TM / Landsat (ThematicMapper) sensor so that the three main components of the transformation contain more than 90% of the information included in the seven initial bands. The interpretation and analysis of these three components by combining them visually or digitally, is simpler and more efficient than using the seven initial bands.The analysis of the main components or other complex transformations can be used as enhancement visual techniques to facilitate the interpretation or to reduce the number of bands that will be provided as input data to a digital classification procedure.

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Image classification tutorial with QGis: 2.2- Images enhancement

To begin with, it is important to understand that image enhancement is applied to facilitate visual interpretation and understanding of images. The enhancement will not change the radiometric values ​​of the objects in the image; it will just allow an observer a better view of these objects. This step, therefore, only serves to help the user define the learning samples and signatures to be used in the classification.

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ArcMap Image Classification Tutorial : 2.3- The spatial filters

 The spatial filters represent another method of digital processing used for the enhancement of an image. These filters are designed to bring out or remove specific features of an image based on their spatial frequency. The spatial frequency is related to the concept of texture. It refers to the frequency of variation of the different tones that appear in an image. The regions of an image where the texture is “rough” are the regions where the changes in different shades are steep; these regions have a high Spatial frequency. The “smooth”regions have a variation of tones that is more gradual over several pixels;these regions have a weak Spatial frequency Space. The spatial filtering method implies moving a “window” the size of a few pixels (eg. 3 of 3, 5 out of 5,etc.) above each image pixel. Then, we apply a mathematical processing using the values of the pixels under the window and replace the value of the central pixel by the result obtained. The window is moved along the columns and lines of the image, one pixel at a time, repeating the calculation until the whole image has been filtered. By modifying the calculation performed within the window, is possible to enhance or remove different types of features present in an image.

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Online help to find the right colours of your map.

Depicting a map is not just a matter of personal taste. If our purpose is essentially to convey certain information in the clearest way possible, we must not forget that the format and final use of the map has to be taken into account when choosing the colour palette. An online site of Penn University will assist you with your choices. The site is   http://colorbrewer2.org/

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Introduction to exploratory data analysis for geostatistics

Here is a small series of articles motivated by a somewhat broad question from a student using ArcGis Geostatistical Analyst: how to interpret the QQplot, the trend analyst and the variogram? Whether it is Geostatistical Analyst or any other geostatistical tool, we are supposed to start, before any interpolation, by the exploratory analysis of the data. Why? Simply because geostatistical tools assume a number of data characteristics and if these assumptions do not apply to our data set, our results will be false. We will discuss what principles are based on geostatistical tools and how to use exploratory analysis tools to support the necessary hypotheses.

  Some principles of geostatistics

Let’s start with the basics of geostatistics. Unlike deterministic interpolation approaches, geostatistics assumes that all values ​​within your study area are the result of a random process. A random process does not mean that all events are independent.

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Image classification tutorial with ArcMap: 2.2- Images enhancement

To begin with, it is important to understand that image enhancement is applied to facilitate visual interpretation and understanding of images. The enhancement will not change the radiometric values ​​of the objects in the image;it will just allow an observer a better view of these objects. This step,therefore, only serves to help the user define the learning samples and signatures to be used in the classification.

Digital images have the advantage of enabling and easily manipulation of the values ​​recorded for each pixel. Although it is possible to perform radiometric adjustments for the effects of solar illumination, atmospheric conditions and characteristics of the instruments used before distributing the images to users, it may be possible the image is not at its best for visual interpretation. Remote sensing systems, especially those using a spatial platform, must be designed to handle the different energy levels of the targets and their environment, which may be found in normal conditions of use. This significant variation in the spectral response of the different types of targets (eg forest, desert, snow, water, etc.) makes it impossible to apply a general radiometric adjustment likely to optimize the contrast and intensity levels in each one of the different conditions. Therefore, a different adjustment of the tones according to the use and the state of each of the images, has to be performed.

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Image classification tutorial with QGis: 2.1 Atmospheric adjustments of Landsat 8 images

We have previously seen how to correct Landsat images for TOA (top-of-atmosphere)reflectance. Here is a diagram that will help us understand the following:

Therefore the reflectance we have calculated is the% of light reflected to the totality of the incident visible light. But as we can see in the diagram,the satellite sensor measures two things at the same time: the light reflected by our targets on the earth’s surface, plus the light scattered by the particles suspended in the air.

We can push the atmospheric correction of the satellite images to remove the light due to scattering.

As we have already said, this is of no interest unless if you work on separate images in time. The diffusion percentage being the same for an image, therefore it is time lost to make this adjustment.

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