New answers tagged remote-sensing
If you really want to use python, and you need functionality similar to GRASS, perhaps the easiest solution would be to use GRASS via Python. That isn't specific to Landsat8, but I don't think a processing solution should be tied that closely to a specific satellite. You could implement some simple wrappers / higher level functions if you're consistently ...
The best source of information on this now defunct satellite is through the European Space Agency (ESA). Here is an ESA link to the Envisat mission. Pay particular attention to the Key Resources section of the web page--here you will find all the technical specifications of the instruments.
One of the best OBIA programs available for feature extraction is called Feature Analyst by Overwatch. There is a good promo video on road and building extraction here. The software is available as an extension for ArcGIS and Erdas Imagine. Unfortunately, this is not opensource software.
you don't need to make an atmospheric correction for the MOD13Q1 product because it was generated by the Maximum Value Composite method,this means that among 16 rasters (16 days) the reflectance value for a pixel is the least contaminated by clouds, but also the one with the maximum value. They provide information on the quality of data VI (vegetations ...
The ground track repeat cycle for ASTER is 16 days, which means every 16 days the pattern of orbit is repeated. So, each year a ground track is repeated (365/16) approximately 23 times. Therefore, you can acquire images of the same area 23 times per year. The orbit period is approx. 98 minutes. Therefore, each year there are (365*25*60 = 547500/98), give or ...
As others have pointed out at the scales you want to work at Landsat data is too coarse. However, one thing you can try which will help a bit is to change the resample technique that is used to display the image. You can do that by following these instructions: Right click on the Landsat layer in the Table of Contents Choose the Properties item from the ...
NY Public Library did this for scanned maps. Their code is open-source. http://www.gislounge.com/automating-extracting-gis-data-scanned-maps/
The reason why it looks pixelated at 1:2500 (and probably at 1:10K or 1:20K) is that you are looking at a single resolution cell (30 metres on the ground, as pointed out by Mapperz) across multiple pixels on the screen. Lets assume that you're looking at a 30m cell at 1:1000 (in true scale, ignore that your monitor probably doesn't really do that) - that ...
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 ...
I haven't seen it, but SOCET GXP claims to be able to do this, based on (potentially) multispectral imagery. It looks very capable, but I assume that capability doesn't come cheap (in terms of up-front costs for the product and the imagery, and in terms of skills development and maintenance).
You might consider using GRASS (which interfaces with QGIS) for this process. See this link for more information: http://grasswiki.osgeo.org/wiki/LANDSAT The answer provided by @markusN on this question provides additional information and examples of what GRASS can be used for with Landsat-8.
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