Automatic image compositing of very large data sets is necessary for the creation of extensive mosaics based on high spatial resolution remotely sensed imagery. A novel morphological image compositing algorithm has been developed which adapts to salient images edges. This technique produces seam lines that are difficult to identify by the naked eye which is also a characteristic to measure the quality of the resulting seam line. It is also shown how updates to an already composited image data set can be easily made without having to reprocess the entire data set.
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In general, compositing refers to the process of combining spatially overlapping images into a single image based on an aggregation function. Mosaicking refers to the process of spatially assembling image datasets to produce a spatially continuous image.
In Earth Engine, these terms are used interchangeably, though both compositing and mosaicking are supported. For example, consider the task of compositing multiple images in the same location. Consider the need to mosaic four different DOQQs at the same time, but different locations. The following example demonstrates that using imageCollection. Note that there is some overlap in the DOQQs in the previous example.
The mosaic method composites overlapping images according to their order in the collection last on top. To control the source of pixels in a mosaic or a composite , use image masks. For example, the following uses thresholds on spectral indices to mask the image data in a mosaic:. To make a composite which maximizes an arbitrary band in the input, use imageCollection. The qualityMosaic method sets each pixel in the composite based on which image in the collection has a maximum value for the specified band.
For example, the following code demonstrates making a greenest pixel composite and a recent value composite:. Use the Code Editor Inspector tab to check pixel values at different locations in the composites. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.
For details, see the Google Developers Site Policies. Google Earth Engine. Developer's Guide. Machine Learning. Geometry, Feature, FeatureCollection. Apps and User Interfaces. Specialized Algorithms. Asset Management. Custom Applications. How Earth Engine works. Point Rectangle It also detects shadows. The last layer is on top. Visualize it with a blue palette. Visualize it with a green palette.
Adaptive Mosaicing: Principle and Application to the Mosaicing of Large Image Data Sets
A claim of priority is made to U. Provisional Patent application Ser. The present invention is generally related to computerized manipulation of images, and more particularly to generation of an image from a plurality of sub-images. Analysis and manipulation of images using computers is well known. For example, computers have been used to analyze images of coins travelling along a conveyor belt to distinguish different types of coins and compute the total value of the coins.
US6137498A - Digital composition of a mosaic image - Google Patents
Compositing and Mosaicking