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. ...
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.
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 ...
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 ...
There are a few good ones around:
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
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 ...
A few months ago I wrote a technical blog post (Planespotting) on intra-detector parallax effects in Sentinel-2 imagery, which can cause aircraft contrails to appear as rainbow stripes. The post also discusses inter-detector parallax effect and motion effects, which also can cause color shifts.
Here is a summary of intra-detector spectral band parallax:
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
Airborne and Terrestrial Laser Scanning
Remote Sensing and Image Interpretation
LiDAR Laser Returns
Laser pulses emitted from a lidar system reflect from ...
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 ...
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 ...
If you look at the product page at LPDAAC, under Layers there is a table that lists each of the bands in the dataset and their characteristics.
For the NDVI layer, it is a 16-bit signed integer with a fill value of -3000, and a valid range from -2000 to 10000. However, there is also a scale factor of 0.0001, or 1/10,000. This means that a value of 10000 in ...
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 at ...
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 ...
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 (it'...
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.
Check out DTclassifier here which you can use with QGIS.
DTclassifier provides simple streamlined interface for raster
classification and change detection using decision trees.
integrated approach — perform all operations including training data collection,
tree-building and classification in QGIS
first example of using ...
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,...
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
Things became more interesting with more spectral bands:
Another book about this topic:
Image Analysis, ...
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, ...
You may consider GRASS GIS which offers a rather complete processing chain for Landsat including radiance correction for Landsat 8. For details, see
Landsat 1-5,7,8 data import
Auto-enhance colors, natural color composites
Calculate Top-of-Atmosphere Reflectance and band-6 Temperature
It is sometimes difficult to distinguish calibration and correction in remote sensing, because we are not in a laboratory with full control on the measurement. Therefore the two are often mixed.
Sensu stricto, radiometric calibration is the conversion from the sensor measurement to a physical quantity. In remote sensing, the sensor is measuring a radiance ...
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For images of the same location but different dates, I would rather talk about compositing than mosaicing (which combines images from different extents into a larger image). You will find a lot of details if you search "compositing" keyword, but here is a short summary:
There are two main approaches for the compositing of time series:
Best available pixel ...
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.
You could try the gdal_fillnodata tool which is also available in QGIS via the Raster->Analysis->Fill nodata menu. It uses an inverse distance weighting (IDW) interpolation. I just tried both that method and the single date Triangulation interpolation (in ENVI) and gdal_fillnodata looked much better. If you want to merge multiple dates, you might have to ...
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 ...
As far as pixel-based classification is concerned, you are spot on. Each pixel is an n-dimensional vector and will be assigned to some class according to some metric, whether using Support Vector Machines, MLE, some kind of knn classifier, etc.
As far as region based classifiers are concerned, though, there have been huge developments in the last few years, ...
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 ...
Firstly, welcome to the site!
Numpy arrays don't have a concept of coordinate systems inbuilt into the array. For a 2D raster they are indexed by column and row.
Note I'm making the assumption that you're reading a raster format that is supported by GDAL.
In Python the best way to import spatial raster data is with the rasterio package. The raw data ...