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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, ...


11

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.


8

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 ...


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 ...


6

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 ...


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

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

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

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 ...


5

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 ...


5

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


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

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). ...


4

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, ...


4

Maybe Opticks, but I haven't tried it myself.


4

My favorite discovery this year has been the Orfeo Toolbox and the associated program: Monteverdi. http://orfeo-toolbox.org/otb/monteverdi.html Lots of options for Remote Sensing work and very helpful documentation. Oh, did i mention it is free and o-s Enjoy, sa


4

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.


4

It will be very difficult to perform an automatic Land Cover Classification based on the 44 class Corine Land Cover nomenclature. but as you say it could be a starting point. You can use the GrassGIS plugin for QGIS - check this- namely for the spectral classification. Don't forget to integrate the ndvi. Also you can try to perform a segmentation of the ...


4

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 ...


4

Keep in mind - no one procedure is necessarily going to provide the "best result." Image interpretation is critical, both before and after classification. You will likely find urban areas misclassified as something else and non-urban areas classified as being urban. You have two basic approaches: 1) Supervised classification: this involves selecting ...


4

Let's narrow down the methods of classification to two major groups: object-oriented classification and pixel-based classification. The attached tables are from a publication titled Comparison of Pixel-Based and Object-Oriented Classification Approaches using Landsat-7 ETM sPECTRAL Bands. The highlighted row in Table 3 shows that object-oriented ...


4

Look at the Layer Values in the Image Object Information window. From here you will be able to determine the pixel value/DN/radiance/reflectance (or whatever your image format is). You will have to add these Layer Values from the Feature View window: Feature View > Object Features > Layer Values > Mean > [right-click] Layer... > Display in Image Object ...


4

It looks like you can manually adjust the ranges by double clicking the Value under the Value column after creating Graduated classes. But the label doesn't update so you have to re-enter the new value there too[0]. [0] - http://hub.qgis.org/issues/9312


4

The basic grouping logic can be handled with if/elif statements and interval comparison. For example: if pointsPerSqMi >= 15: print "Top" elif 15 > pointsPerSqMi >= 12: print "High" elif 12 > pointsPerSqMi >= 6: print "Some" else: print "Low" Replace the print with updating a field once you're satisfied with the logic. ...


4

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 ...


3

I wont be able to give a great deal of information on this, but there are certain rules I think we need to follow for training sites: Training sites should be spread all over the image i.e. should be covering a large area and not just focussed in a region Just as Georeferencing, training sites should be identified at the corners and at points which give ...


3

I just saw this post on QGIS forum and thought I would place here. Hi all. Sorry for crossposting. As some of you know, the r.li suite of GRASS commands allows landscape analyses. Its interface is rather complex, and is still in TclTk, not ported to either wxpython or qgis. As such, it is now more difficult to use than it should be, and it will become ...


3

near infrared band in satellites built for cartography have a meaninfull result bewteen water and other structures. My guess is results based on the size of object would be less than meaningfull, lots of false positives, think flocks of white birds or any other surface or near subsurface objects as well as differences in water temprature, shallow water pans, ...


3

IR is usually most useful for mapping vegetation and water, in as much as water does not reflect ir. My guess is that both your water and beacon will not reflect ir. If you have a relatively homogenous background of water you might try a segmentation approach. You will need a few pixels per feature, so if they are 20-30m structures than 5-m pixels will be ...



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