pixelcooker

Gaps, Hole, or Void problems in a DEM could give some pain in the head for GIS users. gaps will cause the derivated data extracted from a DEM to mess up. Hillshade will lose the color grades, contour line will be jagged and causing a huge amount of wrongly generated lines (makes the filesize raised up exponentially too), slope or aspect data are not accurately mapped, and many others.  Gaps could arise due to the limitation of certain DEM extraction methods, or it happens because of the poor handling of DEM mosaicking. Certain DEM extraction methods like photogrammetric DEM extraction, UAV-based DEM, or interferometric DEM are prone to this gap or voids problem. DEM mosaicking from few sources of DEM that have different map projection is also prone to gaps/voids problem.  Fortunately, many GIS and RS software have provided tools dedicated to fix and correct these holes/gaps problem. DEM editing can be done manually using masking approach or pixel-by-pixel editing, or it could be done a

The ability to create natural color composites is common for the past of current operational earth observation satellites. Natural color composites can be described as RGB image composites that have a color composition just like our eyes perceive color. In natural color composites, water would have a blue color, vegetation is green, bare soil or built-up landcover would have brownish to reddish color. You can make natural color composites images using satellite data that has blue spectral region of the electromagnetic spectrum of the sun. Imagery from satellites such as Landsat, Sentinel-2, IKONOS, Quickbird, Worldview, Pleiades, Kompsat, Formosat, and many others have a blue band so generating natural color composites would be an easy matter.  Things are quite different if we have satellite data that didn't have Blue Band such as ASTER or SPOT. For these kinds of imagery, natural color composites generation must be simulated from the available bands. And the results sometimes woul

Georeferencing is a process to reference geospatial data into known and standardized coordinate system. Georeferencing is part of basic task that all GIS users must have ability to do it. Georeferencing can be done in 2D plane or 3D plane, and it can be applied into raster data and vector data. For Raster data, it could be georeferencing aerial photographs, satellite imagery, or scanned maps in image format. For Vector data, georeferencing usually is called spatial adjustment. Most of GIS and remote sensing software available on the market has this georeferencing ability, either for vector data or raster data.  As I already mentioned above, Georeferencing for raster data can be classified into two kinds of raster data. First is Scanned Maps in Image/PDF format, and second is for Satellite Imagery or Aerial Photographs. For earch type of data, the georeferencing procedures are little bit different. In this article I will talks about georeferencing satellite imagery and aerial photograph