Why is the spatial resolution of my Pléiades/SPOT image lower than expected?
Geospatial data producers frequently provide low resolution multiband images and higher-resolution panchromatic images of the same scene. Remote sensing datasets are usually delivered as more raster bands with various pixel sizes.
For Pléiades and SPOT, each dataset contains a bundle that consists of the individual spectral bands with coarser resolution (2 m for Pléiades and 6 m for SPOT) and the panchromatic band with the higher resolution (0.5 m for Pléiades and 1.5 m for SPOT).
Pansharpening is a remote sensing algorithm that uses the higher-resolution panchromatic image to fuse with the lower-resolution multiband image. This outputs a multiband image with the spatial resolution of the panchromatic image (from 2 to 0.5 m for Pléiades and from 6 to 1.5 m for SPOT).
You can use the Pansharpening algorithm to get a pansharpened image or do it in QGIS.
My image does not look very good, what could be the problem?
UP42 distributes various geospatial data sources and the quality of the geospatial datasets (satellite, aerial and balloon images) is the responsibility of the upstream data providers.
Generally, images captured by spaceborne platforms or aircraft sensors are prone to various types of noise. For instance, noise may be added to satellite images due to atmospheric noise (e.g. aerosols, clouds) or sensor noise (e.g. angle at which the satellite views the ground at any given time). The image generation process can also add noise to the data, because these datasets have to be compressed to reduce their requirements for archiving and data transmission. Another example is salt and pepper noise, which is generated from errors in data transmission and it can affect the quality of the satellite images.
There is no exact method to correct image artifacts, but we recommend using the standard pre-processing methods (atmospheric, radiometric and geometric corrections) and the traditional digital image processing algorithms. There are several methods which can help clean the images from noise, but you should be familiar with the basic concepts. For example, image filtering can enhance the image properties and remove noise. The following filters are the most widely used in digital image processing:
a. Linear spatial filters (average, disk, gaussian, laplacian, log, motion, Prewitt, Sobel, unsharp).
b. Nonlinear filtering (median filters).
c. Fourier Transform.
What is the difference between tasking and catalog images?
Tasking is commissioning satellites to capture new images with specific parameters — over a certain area and at a chosen time frame.
Catalog lets you get already existing images: