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3

There is no specific GIS software for doing this: most will handle the RGB image and the Lidar data. Basically, NDVI is (NIR - RED)/(NIR + RED). Lidar gives you the NIR value and the first band of your RGB image gives you the RED value. Just make sure that your data are calibrated to reflectance (or, if not possible, normalised in the same range of value -> ...


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I'm almost sure that it must be some kind of cloud, since its the only object that I could find that declines in such a constant way after peaking at the visible wavelength spectrum. Thx for the help :)


1

I don't know for what object this particular spectral signature ties at but i can suggest to browse a spectral library for something that is close match. http://speclib.jpl.nasa.gov/


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Actually, it is not granted that you will be able to recover some information from the shadowed areas. However, I once dealt successfully with (cloud) shadows in a hyperspectral image. The aim was simple land cover classification. Here's what I did. I'm not sure how this would work with Landsat images, but since it is very simple you should give it a try. ...


1

A classifier, any classifier, can classify any kind of data. These objects, as Aaron correctly states, can be pixels, objects, superpixels, bananas, sounds, DNA, etc. The main differences which, in my opinion, is really relevant between superpixel- and pixel-based classification are as follows: pixel based : The resolution of the prediction is maximal, ...


1

Its not inexpensive eqipment and it has some limitation but it works in cases like in your question, you could find all details here: Ground-penetrating radar Ground-penetrating radar (GPR) is a geophysical method that uses radar pulses to image the subsurface. This nondestructive method uses electromagnetic radiation in the microwave band (UHF/VHF ...


1

You should provide more details about the task. In general, extracting features from the data image heavily depends on what you are trying to detect/classify, and how are you trying to do it. Here's an example. If you are interested in classifying roads from an urban scene, you may be interested in evaluating large linear filter responses over the whole ...


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@nicksan is right. In its simplest form, spectral (pixelwise) classification is based on a set of examples that you define manually. In most RS-oriented software (ENVI, ERDAS, Orfeo Toolbox, etc), this is based on photointepretation, i.e. drawing polygons or sampling pixel of each ground cover class, modeling them with some learning technique and ...


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This answer might be very late. Yes, you can, and it is not at all difficult. There is a series of blog posts (here, here, here and here) that will guide you through the process. You will need some scripting ability, and can do everything using freely available tools like GDAL, GMT and MODIS reprojection tool. Good luck!


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If you work with R, use gdalUtils package to do it


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use geotiffread function to read Geotiff files with spatial references http://in.mathworks.com/help/map/ref/geotiffread.html you can assign a value (away from data range ex.-99999) instead to the NaN. Else, use interpolation techniques to fill the no data pixel.If you have time series data, you can use TIMESAT or SPIRIT software for filling no data pixels. ...


0

I would start by manually labeling the pixels in an entire scene that are vs are not part of your desired landuse. Then you could try various predictive model formalizations to build models that predict the marked pixels, keeping the model that gives the best results. (There is an entire field of techniques called "model selection" that helps do this ...


4

Forgive me if this is too basic an answer: panchromatic and infrared are mutually exclusive. Panchromatic means all visible light, which is generally considered to range 0.4μm to 0.7μm in wavelength. Near (or reflected) infrared energy is generally considered to range 0.7μm to 0.9μm in wavelength, just beyond visible. See Infrared vs. Panchromatic - Mt. ...


2

ASTER and MODIS both have global coverage and are suited for larger scale flood analysis. ASTER has a temporal resolution of 16 days and contains 14 spectral bands in VNIR (15m), SWIR (30m) and TIR (90m). Note that ASTER SWIR data acquired from late April 2008 to the present exhibit anomalous saturation of values and anomalous striping. MODIS has a higher ...


3

Feature extraction is not always a necessity: it depends on the algorithm used for the classification. Having too many features will lead to the so-called "curse of dimensonnality". A maximum likelihood classifier will be very sensitive to this, while a SVM classifier should in theory handle a large number of features without too much problem. Other ...


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Take an agricultural field as a simple example. Unless we're interested in precision agriculture, in which case characterizing the variability within the field is important, then we're more likely just interested in knowing that the patch of land designated by that field is being used to grow soy, or corn, or whatever crop. That is, from an information ...


3

As many on this forum know, I am often for an R solution. However, in this case it is reinventing the wheel, and in a much less robust way. There is a great piece of free software, Map Comparison Kit (MCK), that implements many published and novel validation statistics for rasters. Of particular interest in this case are the Kappa, fuzzy Kappa and weighted ...


2

Why keep the entire analysis in eCognition? Once you have your image objects derived, export them and run the model in R. You have far more control of the model in R (e.g., model specification, multi-colinearty test, model selection, etc...) and there is no problem fitting a model to all of the data and predicting it to subsets represented by the tiles. I ...


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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 ...


2

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 ...


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The statistics that you highlight do not care if the data is a set of discrete "objects" or individual pixel based. I would also point out that it is quite incorrect to assert that "Random Forests" or "Support Vectors" are object-based and "Maximum Likelihood" pixel based classification algorithms. The model specification is dependent on a response vector ...


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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 ...


2

There is a sample ruleset on the eCognition Community Ruleset Exchange that shows a work-around. Note that you may need to register to access the ruleset exchange link. The rule-set description states the following: This zip archive contains example data to train and apply the classifier algorithm on multiple scenes. At the moment you can do this ...


2

This is not the exact answer but could be used as a workaround. I guess that you are not using the 8 tiles together for memory reason, but your area seems to be quite homogeneous. So you could degrade the resolution of your images (e.g. with a factor 2 or 3) and create a mosaic. Then you train your classifier on the mosaic image and you "save to file" the ...


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I'm guessing you never worked with SAR data before, so I'll break your question down into parts I can answer: 1) Create high resolution DEMs in GIS The process of creating a DEM just from SAR data is quite complex and requires a lot of processing power and memory. I don't know of a GIS software that implements DEM creation due to these constraints. 2) ...



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