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25

I would have to say that the most complete software environment for Machine Learning and nonparametric modeling is R. This is a big field in statistics, spanning K-NN, Kernel smoothing, General Additive Models, weak learners, support vectors, neural nets, semi-parametric spline regression, imputation, etc... I would highly recommend reading: Hastie, T., R. ...


24

IDL is a fantastic stand-alone programming language (you do not need ENVI). I particularity like it for very fast matrix processing on large arrays. @Aaron makes IDL sound much less flexible then it really is. The majority of IDL development came out of the Physics and Astronomy communities. There is robust support for mathematical and statistical ...


21

You could refer this 47 page report by USGS "The Users, Uses, and Value of Landsat and Other Moderate-Resolution Satellite Imagery in the United States—Executive Report" This page gives a list of research works done using Landsat Data.


21

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) ...


20

There are a few good ones around: Orfeo Toolbox GRASS SAGA GIS All with the bonus of being able to be used though the QGIS interface using the SEXTANTE plugin like so http://blog.orfeo-toolbox.org/uncategorized/otb-inside-sextante-inside-qgis


18

With GRASS GIS (http://grass.osgeo.org) you can do these steps: Import Landsat imagery in a GRASS database using: bash scripts: see page bottom at http://www.grassbook.org/examples_menu3rd.php python script in GRASS-Wiki: Automated data import i.landsat.rgb (or i.colors.enhance in GRASS 7) - auto-enhance colors i.landsat.toar (addon for GRASS 6, ...


17

There are a lot of factors influencing GPS accuracy. James Ryan is right about night time and clear atmospheric conditions. Then there is the satellite "layout" on a specific time at at specific position on earth. Depending on how many you can "see" (4 at least for a 3D fix) and their distribution (all in one row is bad, evenly spread out better) your ...


17

I agree with @vascobnunes opinion but if you want to define certain objects you have to use LANDSAT TM because more classification needs more bands as (R, G, B, NIR, MIR, TIR, FIR)... and my choice is that you should use LANDSAT TM (I gave same information in the following explanation) for vegetation. The important thing in this case is that you should look ...


15

For orthorectification: In GRASS see i.ortho.photo. In OSSIM, see OSSIMOrthos.pdf For georeferencing: In Quantum GIS, use the Georeferencing plugin, p 172 of the users guide. There is even an online tool for georeferencing at http://www.georeferencer.org/


15

From a remote sensing perspective, the main benefit of IDL is that it extends the capability of ENVI similar to how the Python arcpy site-package extends the functionality of ArcGIS. If you will not have access to the ENVI platform, consider learning a different programming language. Additionally IDL is a commercial product whereas Python is open-source and ...


14

I have used OpenCV in the past to train for object detection for geo. Orfeo Toolbox is a good open source choice as Vascobnunes pointed out. For a closed-source version, you can take a look at Feature Analyst (that also has an ArcGIS extension). At the end, it boils down to training a support vector machine. There are several libraries that you can use for ...


14

Change detection is a common operation/module in remote sensing packages like ENVI or Orfeo toolbox. It usually involves raster data (satellite images for example). How is the comparison done? With what tools? I feel that the description is not complete. Or something is missing. Change detection is done by comparing two raster images that were taken ...


14

I'd strongly recommend scikits-learn for Python. It supports supervised and unsupervised classification and the documentation is excellent (particularly check out the Machine Learning for Astronomical Data Analysis tutorial and the accompanying YouTube video (note: this is 3 hours long)). The project is under active development, with the last version being ...


14

As Chad Cooper mentioned, what you want to perform is called Object-Based Image Analysis (OBIA). It's a fairly complex process which segments and then classifies an image. There are many programs out there which will perform this for you. However, you will require high-resolution, multi-spectral imagery. Incorporating LiDAR will probably help you out too,...


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

Great Question: Can Time and Position affect your GPS accuracy. Short Answer: YES Why? First lets place your GPS Receiver in perfect conditions where the atmospheric conditions are perfect, there are no multipath effects, no radio interference, and line of sight to GPS Satellites is clear. Your Time and Position on Earth will determine where the GPS ...


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.


13

Landsat is available back to the 80s, it may overlap the dates of your project, excepting of course the 1950s. edcsns17.cr.usgs.gov/NewEarthExplorer/ will let you easily browse the archive, once you apply for a username. With that in mind you could potentially get a series of three satellite scenes, two of which tie in with the aerial imagery. For ...


13

Check out DTclassifier here which you can use with QGIS. DTclassifier provides simple streamlined interface for raster classification and change detection using decision trees. Plugin features: integrated approach — perform all operations including training data collection, tree-building and classification in QGIS first example of using ...


12

The general approach for performing this kind of disaggregation is through dasymetric mapping, which uses ancillary data to inform the spatial distribution of a phenomenon, and is often used for population analyses, such as this one in San Francisco. This paper provides good background on the technique, and if you're working in ArcGIS, scripts have been ...


12

I am in Canada so if I need this imagery I can get it free at Geobase. Elsewhere you should be able to download from USGS direct. You will need to register on both sites. Here are NASA links to download free Landsat data.


12

You may consider GRASS GIS which offers a rather complete processing chain for Landsat including radiance correction for Landsat 8. For details, see http://grasswiki.osgeo.org/wiki/LANDSAT Examples: Landsat 1-5,7,8 data import Auto-enhance colors, natural color composites Calculate Top-of-Atmosphere Reflectance and band-6 Temperature Haze removal ...


12

Panchromatic images are created when the imaging sensor is sensitive to a wide range of wavelengths of light, typically spanning a large part of the visible part of the spectrum. Here is the thing, all imaging sensors need a certain minimum amount of light energy before they can detect a difference in brightness. If the sensor is only sensitive (or is only ...


11

You can't 'remove' clouds from optical imagery, what you see is what you get; they are photographs and there is no optical data recorded from below the clouds in the same way that there is no data underneath building roofs. If you use remote sensing data of a longer wavelength than light such as microwave, the water particles in the clouds do not absorb the ...


11

For processing of Landsat, I can recommend GRASS. I tried many others. You may need to refine your question with regard to the type of imagery you propose to use. There are workflows which have been more or less developed and implemented in various software. Not only the type of imagery, but the purpose of the processing and final analysis. For Landsat, ...


11

There is a new, 30 meters resolution SRTM DTM coming out. As stated on the NASA JPL official page, The next release is planned for later in 2014, and it is expected to include all of South America plus North America south of the United States. It is incomplete, for now, it has only limited coverage. You can read an article about SRTM coverages here (...


11

ESRI has a pretty good help section on LiDAR (below). For more formal details on LiDAR, I would recommend the following books: Topographic Laser Ranging and Scanning: Principles and Processing Airborne and Terrestrial Laser Scanning Remote Sensing and Image Interpretation LiDAR Laser Returns Laser pulses emitted from a lidar system reflect from ...


10

If you only have SPOT 5 and Landsat TM to choose from, money is not a problem and for a small area of 30 000ha, I would agree that SPOT5 is the best choice, although Landsat would have some strong advantages: SPOT5: 2,5 m spatial resolution 3 spectral bands (Green, Red, Near Infra-red) about 2,64€ per sqkm for new acquisitions good revisit time biggest ...


10

One open source option for atmospherically correcting ASTER L1B products, in order to convert at-sensor Radiance values to Top of Canopy Reflectances, is GRASS GIS' i.atcorr module. An implementation of the 6S algorithm in GRASS GIS GRASS GIS features a dedicated module for the task in question called i.atcorr (in GRASS-GIS version 7 or in GRASS GIS vesion ...


10

Form the (i)python basis to the more complex manipulation: Dr M. Disney - Introduction to image data handling These two blog have many examples: Luca Congedo - From GIS to Remote Sensing REMOTESENSING.IO Things became more interesting with more spectral bands: http://www.spectralpython.net/ Another book about this topic: Image Analysis, ...



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