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3

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


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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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I would suggest what Stella said below. You can simply classify it as shadow so it doesn't hurt your accuracy. Of course make sure you have enough areas of interest or training areas of the shadow to make sure that it is all classified. If you have to give a presentation, you can comment that most of the shadow areas are uniform with the near side part of ...



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