When a mosaic dataset containing massive amounts of imagery data is added to the map window, it initially appears in full extent, displaying only the outlines of the stitched imagery. As the map zooms to a certain scale, the mosaic dataset dynamically reads the imagery files within the current display range and stitches them dynamically. The first-time browsing may be slow, but once browsed, the system establishes a cache, significantly improving efficiency on subsequent views.
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| Figure: Full Extent Display of Mosaic Dataset |
When users need an overview of the stitched imagery effect, they can build a mosaic dataset overview, enabling it to display the stitched imagery results at a small scale. For operations related to building overviews, please refer to the Manage Mosaic Data page.
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| Figure: Full Extent Display of Mosaic Dataset (With Overview Built) |
Remove Image No-Value
Some remote sensing imagery, after correction, may contain no-value areas, affecting the stitching display effect. Users can remove these no-value areas through relevant settings. No-value areas can be categorized into two types: internal no-value within the imagery (Figure 1) and no-value outside the valid imagery area caused by correction (Figure 2).
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No-data areas generated by image correction affect the stitched display and should be removed. Additionally, inherent no-data in the data itself is usually unnecessary and should also be removed. There are two ways to remove no-data: No-Value Transparency Mode and Clipping Display Mode. The appropriate method should be chosen based on the specific no-data situation.
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- No-Value Transparent Mode: This method removes inherent no-value in the data. The process is simple: in the Layer Manager, select the mosaic dataset imagery layer, open the Layer Properties Panel, and specify the no-data values according to the image bands. For single-band imagery, simply specify the no-data value; for multi-band imagery, the no-data value corresponds to the composite RGB value. After setting the no-data value, check the No-Value Transparency option to remove no-value areas.
- Clipping Display Mode: Once the image clipping area is obtained, it can be used to control the display of the valid image range. The specific operation is as follows: in the Layer Properties Panel of the imagery layer, go to Advanced Settings -> Clipping Type, and set it to Data Clipping. At this point, the sub-dataset used for clipping will be applied to display the imagery.
Figure: Clipping Display to Remove No-Data Caused by Correction
Display Effect Settings
The display effect settings for mosaic dataset imagery are similar to those for regular imagery. Common adjustments include special value display, color table display, and stretch display. Additionally, raster functions can be configured to quickly generate 3D hillshade maps and orthophoto imagery. These settings are applied through the Image Layer Properties Panel.
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| Figure: Special Value Display Effect Settings |
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| Figure: Color Table Display |
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| Image: Stretch Display |
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| Figure: Applying 3D Hillshade raster function |
- Display of special value: For special values in imagery, they can be treated as transparent or replaced with another color. In the Image Layer Properties Panel, use the Background Value Settings to define special values. For multi-band imagery, the special value is the composite RGB value. After determining the clipping area, choose the special value display effect and either check Background Transparency for transparency or select a replacement color for special values.
- Color Table Display: Mosaic datasets managing DEM data can use color tables to display elevation gradations, making visualization intuitive and aesthetically pleasing.
- Image Stretch Display: Stretching enhances image clarity and can be combined with color tables for better visualization.
- Raster Function Display: When managing large-scale DEM data, raster functions enable rapid generation of 3D hillshade maps and orthophoto imagery.
Visible Bound Settings
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Using Image Layer Display Filters, certain mosaic imagery can be filtered and displayed based on the attribute fields of the footprint sub-dataset.
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Mosaic datasets can rapidly configure national or global image maps. However, sometimes, only a localized image needs to be displayed. For example, if an image map should only show data within Hebei Province, the Boundary Sub-Dataset of the mosaic dataset can be used to achieve this quickly.
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The Boundary Dataset controls the display range of the mosaic dataset and is constructed based on the footprint by default. It can be rebuilt using the Hebei Province Administrative Boundary Dataset or other methods, such as drawing arbitrary polygons or selecting area objects from the map. Once rebuilt, the boundary updates to match Hebei’s administrative boundary
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| Figure: Rebuild Boundary |
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| Figure: Before and After Rebuilding the Boundary Using Area Objects |
- In the Image Layer Properties Panel, set the Clipping Type to Boundary Clipping to display only the imagery within Hebei Province.
| Figure: Before and After Rebuilding the Boundary Using Area Objects |
Display Order Settings
By adjusting the Display Order of Image Layer Objects, the display order of mosaic imagery can be modified. This is controlled by modifying the SmZOrder field in the Footprint Dataset. For details, refer to the Object Display Order Field section.
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| Figure: Setting different Display Order |
Displays performance optimizations
To enhance the display efficiency of mosaic datasets, consider the following three methods:
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The most fundamental way to improve the display efficiency of stitched imagery in a mosaic dataset is to create image pyramids. Mosaic datasets support batch processing to create pyramids for managed imagery. For detailed steps, refer to the Manage Mosaic Dataset page.
- Enabling File Handle Caching for Imagery Files in Mosaic Dataset:
File Handle Caching: Each time a mosaic dataset is displayed, the system opens the image file handles for reading, which incurs performance overhead and reduces rendering speed. Enabling file handle caching reduces the number of file handle operations, improving performance. However, caching file handles increases memory usage. By default, the system caches handles for 100 files, but users can adjust this value based on their dataset size and system performance.
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