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It depends upon the intended use of the Landsat data. Generally speaking, if you are doing multi-temporal analyses, you need atmospherically corrected data, otherwise DN format is sufficient. I would recommend reading the following landmark paper on the subject: Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). ...


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Draw polygons with ArcGIS. The polygons are areas with a specific type of vegetation. You have some areas FROM Landsat 7 and MODIS. You want to "export" the spectral signature of these areas. With ArcGIS you can use "zonal statistics as a table" to extract the mean spectral values for each band and within your polygon. You can run it in batch mode (right ...


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Well, thank you so much @Aaron. The problem has solved. I have realized that the above code needs to be slightly modified for the case of Google Earth imagery regarding its conversion to grey-scale intensity image. Following is the output of my modified code. Impervious surfaces of building rooftops have been extracted. Nevertheless, the results can be much ...


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I have also had a lot of success using the image classification tools in ArcGIS (http://resources.arcgis.com/en/help/main/10.2/index.html#//00nv00000008000000). The documentation is great and the results have been very accurate. Unsupervised classification is tricky because defining the number of classes will always result in some degree of mixing. Even ...


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You can use the OpenCV package in Python for image thresholding. This example shows not only how to perform the binary image thresholding, but also the limitations of this method. Here, I use a 1m spatial resolution NAIP image that shows a dirt road surrounded by arid vegetation. You can see that the road is extracted but there is also a significant amount ...


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If you use QGIS, there is a plugin named "semi automatic classification". It may not take too much time to utilize the plugin because you might be familiar with the RS analysis methods. I have used it for 1 week and have been pleased. The plugin is also capable of downloading landsat's photos. Here's the link of classification tutorial. ...


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I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


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You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...


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As far as i know there's no way of performing atmospheric correction for L8 images in Arcgis 9.3. but there's an plugin i QGIS called Semi-Automatic Classification that will perform DOS on Landsat 8 images. Check it out, it's pretty good. You can find all the information here: http://fromgistors.blogspot.com/ and also in the Semi-automatic Classification ...


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Just solved it. Uninstalled and intalled SciPy module (http://www.kyngchaos.com/software/python) again and it worked.


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Use Extract Values to Points in Spatial Analyst Extension. Also you can use Extract Multi Values to Points. You need a shapefile with the points of interest. This tool add the values of the raster to the attribute table of the shapefile.


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I would advise using the dark object subtraction (DOS) method for atmospheric correction. Essentially, you find a dark object in your scene--such as a deep, dark water body--where you know there is no reflectance. Any brightness values associated with the dark object in your scene are therefore likely the result of atmospheric effects. These values can then ...


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you can embed Orfeo Toolbox in Python in order to process your remote sensing data, including applying masks. There is a Python interface for OTB and the Bandmath can be used to apply a mask. You can also use gdal for the same purpose, it also has the tools necessary for masking an image (see gdal_calc.py) and there is also a python interface.


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I had some success by changing the target directory to which I saved the signature file. Rather than using the server space I had been alloted, I chose the local desktop, and it worked without a hitch. Not sure why.


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The DOS methods are for atmospheric correction only, not radiometric correction. The i.*.toar modules allow you to combine in one step radiometric correction with some additional DOS atmospheric correction method. The input to the i.*.toar modules is the original DN values. The default to i.*.toar is "uncorrected", so normally you would use i.*.toar to get ...


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I just tried with combine (local in Spatial stat tools) and it worked just fine (need to be carefull with the raster parametes in each date data set), the first column shows the first year, whereas the second shows the last (LC change analysis). Now I'll compare these values with the ones I got from Idrisi land cover change modeler and see if they're the ...


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1. where should I begin? Do you know what Image Classification is? If not here's an intro article ESRI wrote about for arcgis. You don' need arcgis to read it. Read it, and in the end you'll understand what you should need. Keep in mind that image classification is about creating classes. To do that should well defined classes beforehand (how many, ...


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You, in theory, can do this but I would question the reliability and replication of this approach. Most digital cameras are not calibrated so, it would be quite difficult to standardize the imagery to make them directly comparable through time and space. If you only intend to acquire a single image or are not planning on comparing data, there would be no ...


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Landsat satellite is the best one because it can be used even for near infrared light


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In short, yes. You can do that. The sensors in most cameras are sensitive to light from UV to IR. To change the information into the standard RGB, most cameras use a Bayer Filter (see the Bayer Filter wikipedia for more info on how this is done) approach to filter the visible light into red, green and blue, while throwing away UV and IR information. As ...


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Indeed, the results of a single-geometry InSAR analysis yield Line of Sight deformations. When using a double geometry (both imagery in acscending and descending directions) the vertical component can be computed. The horizontal deformation measurement is in that case sensitive in the East-West direction en far less sensitive in the North-South direction ...


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Which satellite? Landsat 8 for example: see the bottom bit here http://landsat.usgs.gov/Landsat8_Using_Product.php


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you can perform atmospheric corrections on the MS and PAN bands, then execute pansharpening. It will yield better results that way



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