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13

I use a number of tools depending on the type of classification I am trying to perform. For general unsupervised/supervised classification I use ENVI, which has many options for classification methods (including some more advanced methods using neural networks and support vector machines). It is very easy to extend ENVI using the IDL programming language, ...


13

You can use GRASS GIS for this which supports texture extraction and image classification based on a radiometric/segmentation approach. For an idea, check this conference abstract, a planned talk at the Geoinformatics FCE CTU 2011. See also: http://grass.osgeo.org/wiki/Image_processing and http://grass.osgeo.org/wiki/Image_classification for an overview.


11

Equal_Interval "the range is then divided by the number of classes" http://wiki.gis.com/wiki/index.php/Equal_Interval_classification Quantiles " for visualizing continuous data that is not distributed normally" http://wiki.gis.com/wiki/index.php/Geometric_Interval_Classification Natural Breaks "method designed to optimize the arrangement of ...


9

You might want to try Orfeo Toolbox. OTB is based on the medical image processing library ITK and offers particular functionalities for remote sensing image processing in general and for high spatial resolution images in particular. Targeted algorithms for high resolution optical images (SPOT, Quickbird, Worldview, Landsat, Ikonos), hyperspectral ...


8

The term spectral signature refers to the relationship between the wavelength (or frequency) of electromagnetic radiation and the reflectance of the surface. The signature is affected by several things including the material composition and structure. Some parts of the EMR spectrum, such as the microwave region, are more sensitive to surface structure than ...


8

In terms of something akin to a spectral signature, the only way would be through the return intensity values, which are rarely calibrated. Unfortunately, there is really nothing expected in the characteristics of the return intensity that would separate rock and soil, the answer really is that this is not a likely outcome. Now, if you used surface texture ...


7

Yes. The following technique is general and works in all relational databases and (therefore) in many GISes. Create a lookup table describing the reclassification: it will look exactly like the table in the question. Joining the lookup table to your attribute table on [Field1] completes the job. This solution is fully automatic: any update to the ...


7

I have tried supervised classification in ArcGIS. Firstly I would say that it is not the best software for classification. As I did it, you can create training sites as points. Just create a shapefile (or geodatabase), add Integer field, click points over your image and assign classes as numbers. (I think you can also use polygon shapefile). For ...


7

As troubleshot by @Paul, the error message is being triggered because you have placed your *.gsg file inside of a file geodatabase folder (*.gdb). It seems like the Maximum Likelihood Classification tool is getting confused by this. However, the error can be easily avoided by ensuring that your *.gsg file is NOT inside of a file geodatabase folder (*.gdb). ...


6

First of all I should mention that this question is addressing very limited space, though important question too. The first thing that comes to my mind on this subject that you can consider temporal information in short time interval. if certain values are changed in certain areas, it may be easier to detect snow. and second solution is here from National ...


6

For a fantastic way to detect, visualize, and report your findings to the public, check out the Landtrendr (Landsat-based Detection of Trends in Disturbance and Recovery) program from OSU. The Landtrendr program is one of the most exciting recent developments in change detection research. There is very good documentation on the methods, and Landtrendr code ...


6

I suppose you want to do this in ArcMap. Right click -> Properties -> Symbology You will need to select the Classified and finally classify in the way you want. For more info check the ESRI help file


6

It is really is worth the time to learn the code interface. Here is some annotated code for specifying a simple Random Forest image classification using spatial data. # Add required libraries require(randomForest) require(sp) require(rgdal) require(raster) # SET WORKING DIRECTORY setwd("D:/ANALYSIS/Kenya_Hirola/RandomForest") # Read point shapefile with ...


6

those are different things. Image classification is the process of creating a thematic image where each pixel is assigned a number representing a class (can include the class 'unclassified'). In an aerial image the classes can be soil, vegetation, water etc. image classification algorithms examples are k-means or ISO-DATA. Pattern recognition is the ...


6

In QGIS 2.4 there is a Standard Deviation mode in symbology like on the picture below: You have to choose the attribute column of the data to be presented, the number of classes you wish to have and the colour ramp with two different colours. You can also define custom intervals if you would like to, just be sure that you edit the labels too, so they will ...


6

A spectral signature is some measurable quantity (e.g., reflectivity, emissivity), which varies as a function of wavelength and can be used to identify a material. To obtain a signature, the quantity must be measured at a sufficient number of wavelengths (and at fine enough spectral resolution) such that the material can be discriminated from other ...


6

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with: Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...


6

You can use Data defined override in combination with categorised symbology. With this approach you can do categorised symbology on one attribute and set some other parameter (line style, line width etc.) to change according to other attributes. example on OSM road data: classified by attribute "type" (line color) change the symbol and choose some ...


5

According to this article, looks like something similar to this has happened before and Google claimed it to be a imagery processing mistake.


5

The online help for Arcmap 10 shows some field calculation examples using Python code blocks, to reclass data. If you didn't have to many cases, the example could provide some guidance. See the section called Calculate fields using logic with Python and Classify based on field values near the middle of the link.


5

For an open source GIS solution, see here: http://grass.osgeo.org/wiki/Image_classification It includes a small tutorial as well.


5

Could be a cloud. Google try to filter these out, but not too well in this case. Have not found any links to back this up, but I do remember reading about it on a post that was looking at the possibility of rendering a 3D real-time cloud layer.


5

If I understand you correctly, you are looking for a supervised classification procedure. Some theoretical background: http://rst.gsfc.nasa.gov/Sect1/Sect1_17.html This is certainly possible through grass: http://grass.osgeo.org/wiki/Image_classification#Supervised_classification_2 As an alternative you could also look at saga (I'm not saying it is better, ...


5

I was going to suggest SPRING. SPRING although it's a clumsy software it is very good at what it proposes. It has very interesting algorithms. Maybe GRASS can handle the job, but AFAIK, GRASS is mostly a command line package.


5

Creating landuse database, you need sensor technologies to detect and classify objects. i think you already know that: There are two main types of remote sensing: passive remote sensing and active remote sensing. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most ...


5

At the start of your question you ask about going from 32 bit to 8 bit and at the end you ask about going the other way, so this will be a generic answer. Most of the GDAL functions allow you to specify the pixel depth with the commandline tag -ot (for instance see the documentation on gdal_translate or gdal_rasterize). The -ot switch can take the values ...


5

By default, ArcGIS samples for classification by taking the first 10,000 records. This can be changed in the classification dialog by increasing the number of records used. For information on the implementation, I'd recommend seeing this mapping center post (and read Charlie Frye's comments since he worked on the original implementation)


5

Yes, image classification is generally improved when you remove topographic illumination effects (same goes with atmospheric effects). However, as with anything like this, there exists a wide range of techniques for accomplishing this task and the effectiveness of it will depend both on the sophistication of the technique (its ability to model the physical ...


5

Classification algorithms such as Maximum Liklihood, random forests, and SVM are statistical methods for grouping data. These data may be words, colors, sounds or anything you can imagine. In a remote sensing context, these algorithms are used to group pixels or image objects (segments) based on statistical properties, or spectral profiles. To answer ...


5

NDVI is for vegetation/non vegetation discrimination. So if your vegetation is always coniferous forest, then it should be the most efficient method in your case. Otherwise you will have confusions with crop, grassland and deciduous forests. In a montainous area, single reflectance thresholds will be problematic due to the hillshade (clearly visible on ...



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