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7

Erdas used to work together wih ESRI, but now it is ENVI that has joint its forces. I would therefore use ENVI for the compatibility. But if you are looking for an good open source solution, I recommend Orfeo Toolbox (http://orfeo-toolbox.org/otb/ ). You can either use the library, the command line application or a complete GUI (called Monteverdi). ...


7

There is no way to get floor heights from a lidar pointcloud. Lidar is captured by bouncing lasers off the groundsurface and measuring the bounced back pulses. Therefore there is no way for the lasers to 'see' through the roof of a building and return a floor height. However, a solution to this may be to classify your las point cloud into ground and non ...


6

In arcgis10 when you add images to a raster mosaic dataset there are options to create footprints and metadata.


5

When remote sensing vegetation, the time of year is very important. In most climates, vegetation has significantly more biomass (i.e., leaves etc.) during the summer, which means that it is easier for the sensor to discern the health of vegetation at that time of year. Two NDVI images of the same location from different times of the year may look different ...


4

There is nothing built into the software that can solve differential equations. I am assuming that your values are derived from spatial data or you would be posting this on another site. Your best bet (if you are tied to one of these software packages) is to write some code in ArcPy that pulls in your values and does the math. The NumPy Python library, ...


4

Though I am not able to understand the difference between the standard deviation output and the percentage output and what is the significance of using one over the other? Those refer to the threshold used to decide whether there has been any change between two images. For percentage change, it uses a symmetric relative difference formula to ...


4

From the USGS FAQ: the blue band is useful for "Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation". It's my experience that you get better results by using band combination, however.


4

You can find the system requirements for ArcGIS 10.2 (the latest version) here and for ERDAS here. The laptop you list more than satisfies the minimum requirements. For schooling you probably won't need a powerhouse and the machine you list will be more than adequate. In the event that you want to upgrade here are some things to consider: Processor: an ...


3

For specific remote sensing tasks you could check out BEAM. If you are not afraid of command line, I would suggest a combination of GRASS (for storage and datahandling and analysis), QGIS(for visualization) and GDAL/OGR and pktools (for analysis). All these are open-source. A very good instructional site is here.


3

Idrisi Selva through the Clark University Lab is an amazing alternative for image processing. I think there are ArcGIS plug-ins for it as well.


3

If you have gdal command line setup you can try this: gdal_translate -outsize xsize[%] ysize[%] <src_dataset> <dest_dataset> Example: creating 25% of original image. gdal_translate -otusize 25% 25% input.tif output.tif ......


3

you can try doing object based classification based on size and signatures of the vehicles and look at the results. Then you can remove vehicles from the image. Afaik, there is nothing that will do it in one click.


3

I would recommend calculating soil moisture indices from Landsat TM bands. MTRI has an interesting article on creating soil moisture index (SMI) from Landsat TM 5. Also, I would recommend exploring soil moisture estimates using TM band 6 (Thermal IR). Attached is a good tutorial on calculating indices from Landsat TM bands using ArcGIS 9.x (as you ...


3

Penn State offers a wide variety of free online classes (for no course credit). You can take a look at the Penn State Online Geospatial Education Program Class Calendar. From there you may want to take a look at: Geog 883: Remote Sensing and Image Analysis and Applications: An intermediate-level course focusing on the use of remotely sensed imagery in ...


3

I have not tried it but Opticks is worth a shot: http://opticks.org/confluence/display/opticks/Welcome+To+Opticks


3

ENVI has never been very good with formats other than the native bil and tif. I have seen the behavior you mention, but it is inconstant and dependent on how the file was saved into an img format. It would be good to know how you are saving the file. I find it very unstable to just give output an img file extension. Your best bet is use the "Save File as ...


3

Using Erdas, the Sieve tool is located: Raster tab > Thematic (Raster GIS group) > Sieve Also, a widely accepted approach is to use GDAL's gdal_sieve.py, describes as follows: The gdal_sieve.py script removes raster polygons smaller than a provided threshold size (in pixels) and replaces replaces them with the pixel value of the largest ...


3

Reproducing the map example you provided is primarily a cartographic effort and requires very little analysis if you have already calculated NDVI. I would use the following workflow to produce the map similar to the one you provided a link to. Collect the NDVI data to use in your analysis. In the example, they use "Summer" 1989 to 2001. In your case, ...


2

you can check out Qgis Sextante Toolbox in plugins repository. it has lots of geoprocessing alagorithm in it. SEXTANTE is a spatial data analysis library and a powerful geoprocessing framework.The main aim of SEXTANTE is to provide a platform for the easy implementation, deployment and usage of rich geoprocessing functionality. It currently ...


2

You can open the raster image as a GDALDataset : poDataset = (GDALDataset *) GDALOpen( pszFilename, GA_ReadOnly ); Then get the raster band containing the color codes with : GDALRasterBand *poBand; poBand = poDataset->GetRasterBand( i ); where "i" represents the raster band id. Then iterate over the raster band pixels and read each pixel color code value ...


2

There are several good sources including the following: WELD WELD generates 30-meter composites of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) terrain corrected (Level 1T) mosaics at weekly, monthly, seasonal and annual periods for the conterminous United States (CONUS) and Alaska. These mosaics provide consistent data that can be used to ...


2

When you move any rasters referenced by a mosaic dataset, including overviews, you must repair the paths locating these rasters.


2

GRASS GIS (open source, since version 6 with a new graphical user interface) offers many image processing methods including: Import of all common satellite, aerial and UAV data formats Preprocessing Geometric preprocessing/Georectification Radiometric preprocessing Correction for atmospheric effects Correction for topographic/terrain effects Cloud ...


2

The overall hardware specs should be more than sufficient for basic tasks. However, note that the display's vertical resolution of 900 pixels can be quite limiting during work with maps or satellite / aerial images. You could consider a notebook with higher resolution, e.g. a 1920x1080 display or similar. At least I recommend to do a hands-on-comparison of ...


2

I have an "older" 2011 HP with the specs show below. Consider this as a baseline for work with Erdas and ArcGIS 10.2. In other words, do not go with a system with less capability than this one as the lack of performance will likely be noticeable. Realistically, any heavy geoprocessing you do is often on a school computer or a via VPN connection to a ...


2

Idrisi - not free but cheap and there is a free trial version. Good for raster comparison and change direction. http://www.clarklabs.org Or saga! Not simple but very powerful and free! http://www.saga-gis.org/


2

with ArcGIS, you can use the "copy raster" tools to convert the img file in a ENVI file. Just write ".dat" at the end of the name of the output file. With ENVI you just need to convert to an ENVI file and use the file that has no extension. By the way, you don't need to write code to use gdal. You can install it with OSGEO4W, then you type gdal_translate ...


1

If you're new to remote sensing, I would recommend a couple books that are standard for beginners in the field. Remote Sensing and Image Interpretation - Lillesand, Kiefer, Chipman Remote Sensing of the Environment - Jensen These books are a great introduction to RS and will be infinitely more valuable than software tutorials for those who do not yet ...


1

You can do it by this way: 1. Go to 'Image Interpreter >>> Utilities >>> Change detection'. 2. Give before and after images. 3. It will produce the 'Difference' image as well as 'thematic image' of five classes namely decreased, some decreased, unchanged, some increase and increased. From which you can calculate the size of your decreased, increased and ...



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