![]() You can also see the sides of buildings rather than just the roofs. There are advantages to this and one is that it allows you to measure the heights of buildings based on their shadows, creating a more natural-looking image. The result is a smooth transition from one image to the next.Ī great deal of high-resolution imagery is taken from a viewing angle that is not directly overhead. However it is set up, when you choose this option, it generates the seamlines, ranks your images based on the distance to those seamlines, and chooses the closet imagery. They can be based on the footprints of the imagery or the features within the imagery. When you put your collection of imagery together, there are a couple of options for identifying seamlines. The Seamline method is another option that's used for moving around your imagery. As you zoom in to a feature, the selection updates automatically, so you see the imagery closest to the feature. For example, when you choose the Closest To Center method, you will be looking at the images closest to the center of your screen. Other sort methods can be used for zooming in and panning around your collection of imagery. The None method orders the images in the same order as the attribute table. Whenever there is overlap, choosing the North-West method resolves it by choosing the layer closest to the northwest corner of the boundary. In this case, the North-West and None methods are good options. You don't need to do any analysis you just need to have it in some kind of logical order. Some of the sort methods are particularly suited for having your collection of imagery be just a collection of imagery. These rules are known as sort methods and are located on the Sort drop-down list. You can set up rules that apply to each layer as a whole, or you can apply rules to only the parts of the imagery that are overlapping. Deciding how to resolve conflicts from overlapping imagery is a component of working with collections of imagery. Conversely, when working with higher-resolution imagery, the revisit cycle is not systematic, and you may get imagery that overlaps in some areas. MODIS collects imagery systematically, where the imagery is collected according to a defined periodicity and grid alignment however, with a resolution of 250 meters, those pixels may be too coarse for some remote sensing questions. The Raster Item Explorer option can be used to filter items from a large collection of imagery in either a mosaic dataset or an image service and explore the properties of individual items, add them to a map or scene, and view and edit the processing applied to an item. You can then switch the selection, which selects all of the images from January through June and September through December, and add this to the map as a different group. After making the first selection, you can group the selected imagery and add it to the map. You selected all of the Landsat scenes with minimal cloud cover from the months of July through August and you want to compare what the vegetation looks like for the rest of the year. Once you've made the selection, you have a choice of how you want to display those scenes. The Rectangle tool is good for imagery since typically you don't work with scenes with irregular shapes. In addition to making a selection based on an attribute, you can make selections by drawing around the scenes in question. These are the kinds of selections you can make using Select By Attributes. You may want to see images taken during a certain season, or you may want to look at scenes that have minimal cloud cover. In this collection, you have multiple scenes for each date that cover the entire state, and those images were collected on different dates. In the southwestern United States, much of the annual rainfall occurs over a few weeks during the monsoon season. If you have a collection of images over a city that comes from a variety of sensors taken at different times of the day on different dates, you can make a selection based on any of these characteristics, also known as querying by attribute. On the Data tab, you have options for selecting the specific layers with which you want to work. Selecting layers from a collection of imagery Resampling, and mosaic method, are modified on this DisplayĪnd rendering properties, such as stretch, band combination, Rendering of the mosaicked image similar to a raster layer. Single polygon, which is multipart if your collection of imagery is not In this table, you can sort your imageryīased on any of the attributes, such as cloud cover, acquisitionĭate, or any of the sensor characteristics.Įxtent of all the raster datasets in the mosaic dataset as a The footprint attribute table is the catalog ofĪll the images in the mosaic dataset in addition to any associated The extent of each item in the mosaic dataset as aĭistinct polygon. Selecting layers from a collection of imagery.
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