Inspect and visualize imagery

Imagery is an important type of data used in most remote sensing-based workflows. Imagery can be any optical satellite or aerial image. You can use it as a backdrop for other layers or analyze it to extract information beyond what the eye can see. In this introductory tutorial, you'll learn how to understand and display imagery in ArcGIS Pro. First, you'll inspect the properties of an imagery dataset to learn more about its cell size, pixel characteristics, and spectral bands. Then, you'll change the image's appearance to make it smoother and more vivid. Lastly, you'll change the band combination to better distinguish vegetation in the image. The skills you'll learn in this tutorial can be applied to nearly any imagery workflow in ArcGIS Pro.

This tutorial was last tested on April 9, 2026, using ArcGIS Pro 3.6. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS Pro (see options for software access)

Inspect and visualize imagery

Inspect the image properties

First, you'll download a project package containing the data for this tutorial. Then, you'll inspect the raster dataset properties of the project's imagery data to learn more about it.

1. Download the Hallstatt_Imagery project package.
2. Browse to the downloaded file and double-click it to open the project in ArcGIS Pro. If necessary, sign in using your licensed ArcGIS account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   The project appears in ArcGIS Pro.

   

   The project contains an image of Hallstatt, a small town in the Austrian state of Upper Austria, located along the national road linking Salzburg and Graz. This picturesque and historically important town is highly impacted by tourism, and its 780 citizens are hosts to more than 10,000 daily visitors. The map also contains two basemap layers that provide geographic context for the surrounding area.

   Note:

   The image used in this tutorial comes from the State of Upper Austria, which provides various regional vector and raster data layers under a Creative Commons Attribution 4.0 Austria license. If you have any questions about this data, use the following contact information:

   • Office of Upper Austria. State Government
   • Directorate Presidium, Department Presidium, Landhausplatz 1, 4021 Linz
   • Telephone: [phone]
   • Fax: [phone]
   • Email: [e-mail]

   The image, Hallstatt_Image.tif, is a multispectral aerial image that contains four spectral bands. In the Contents pane, the image's legend lists three bands that are currently displayed: Red: Band_1, Green: Band_2, and Blue: Band_3. Band_4 (near infrared) is not currently displayed.

   

   Note:

   Each band is represented by a raster: a grid of cells (or pixels) organized into rows and columns in which each cell contains a value. The bands are stacked and together form a composite image dataset that displays in color. To see the raster grid, you can zoom in to the image with the mouse wheel button until you see the individual pixels.

   To learn more about spectral bands, try the Explore imagery - Spectral resolution tutorial.

   Next, you'll inspect the image's properties.

3. In the Contents pane, double-click Hallstatt_Image.tif.

   

   The Layer Properties window appears.

4. Click the Source tab.

   

   The Source properties list various details for the layer source, such as the data location on disk and the number of rows and columns of pixels.

5. Expand the Raster Information section.

   

   The Columns and Rows properties show how many pixels are in the x direction and y direction, respectively. You can multiply the columns and rows to find how many pixels are in the entire image.

   The Number of Bands property indicates that there are four spectral bands. You already learned about the bands in the image.

   The Cell Size X and Cell Size Y properties show the pixel size, or resolution, of the raster.

   Note:

   To learn more about cell size, try the Explore imagery: Spatial resolution tutorial.

   In this case, the cell size is 0.2. Usually, the cell size is the same for x and y, meaning each cell (or pixel) is a square. But what is the unit of the 0.2 value? You'll review the Linear Unit property to determine the unit.

6. Expand the Spatial Reference section.

   The Linear Unit property value for this raster is Meters (1.0). Each pixel represents a surface on the ground of 0.2 by 0.2 meters, or 20 by 20 centimeters.

   

7. Collapse the Spatial Reference section. Scroll back up to the Raster Information section.

   The Pixel Type property value is unsigned short, which means that each band of the raster contains only positive pixel values. The Pixel Depth property value is 16 Bit, which means that a pixel can hold 65,536 different values. Since the type is unsigned, the values can range between 0 and 65,535.

   

8. Collapse the Raster Information section and expand the Statistics section.

   

   Statistics are reported for each of the four bands in the raster dataset. Raster statistics include the minimum and maximum pixel values, as well as the mean and standard deviation of the pixel values. Statistics are needed to properly symbolize and render raster data.

9. Collapse the Statistics section and expand the Spatial Reference section.

   

   The Projected Coordinate System property is set to MGI Austria GK Central, a coordinate system commonly used for this region of Austria. This coordinate system is the preferred choice for the project, so you won't have to change it.

   Note:

   To function in a GIS, every layer needs to be defined within a coordinate system. Learn more about coordinate systems and projections in the Choose the right projection tutorial.

10. Close the Layer Properties window.

Inspect pixel-level information

Next, you'll explore the composite image using the Image Information pane, which shows pixel-level information about imagery.

1. In the Contents pane, confirm that Hallstatt_Image.tif is selected.
2. On the ribbon, click the Imagery tab. In the Tools group, click Image Information.

   

   The Image Information pane appears. The pane contains contextual information about an image based on specific pixels you point to on the map.

3. On the map, point anywhere on the Hallstatt_Image.tif imagery layer.

   The Image Information pane updates with the following information:

   • The Location section shows geographic information about the current location of the pointer.
   • The Spectral section shows the image's spectral information at the location of the pointer, in particular the value of the current pixel for each band.
   • The Quick View section shows information about the sensor that captured the image if that metadata is available. (For this image, this information is not available.)

   

4. On the map, point to the lake (water) on Hallstatt_Image.tif.

   

   In the spectral information, the Band 1 (red) and Band 4 (near infrared) values are low, as is typical for water.

5. Point to a dark green, forested area (dark green).

   

   The near infrared band value is particularly high because the cell structure of healthy vegetation strongly reflects near infrared light. These variations in band values make it possible to identify different types of land cover by changing the bands displayed in the image.

6. Close the Image Information pane.

Optimize visualization

Next, you'll make changes to how the image is displayed to optimize its appearance. Before you do, you'll create a copy of the image to compare the changes you make to the original image.

1. In the Contents pane, right-click Hallstatt_Image.tif and choose Copy.

   

2. In the Contents pane, right-click Hallstatt Map and choose Paste.

   

   A copy of the image is added to the Contents pane. You'll rename it to distinguish it from the original.

3. Click the name of the copy to make it editable. Rename the copy Hallstatt_Duplicate.tif and press Enter.

   

4. Drag Hallstatt_Duplicate.tif under Hallstatt_Image.tif. Click Hallstatt_Image.tif to select it.

   

   Next, you'll change the appearance of the original image by trying different types of image stretches. Image stretches take the original value range of an image raster and spread (or stretch) it to take advantage of all the possible values offered by the raster's bit depth. For instance, the values in a raster may originally vary from 34 to 148. When a stretch such as Minimum Maximum is applied, the values will be transformed to use the entire range, from 0 to 255. This difference will make the image appear more vivid and contrasted.

   By default, both Hallstatt_ Image.tif and Hallstatt_Duplicate.tif use the Percent Clip stretch, which is a good default stretch for imagery. You'll compare that stretch to Minimum Maximum.

5. On the ribbon, click the Raster Layer tab. In the Rendering group, click the Stretch Type drop-down menu and choose Minimum Maximum.

   

   The change is applied, but it's subtle. You'll use the Swipe tool to compare the two images.

6. In the Compare group, click Swipe.

   

   You can use the Swipe tool to pull back the layer that is selected in the Contents pane to reveal what is below. In this case, the Swipe tool pulls back the Hallstatt_image.tif layer to reveal what the Hallstatt_Duplicate.tif layer looks like. This allows you to compare the two layers.

7. On the map, place the pointer near the edge of the image. Slowly drag the pointer from top to bottom or left to right.

   

   The image with the Minimum Maximum stretch is slightly less vivid and has less contrast. As a general rule, applying some type of stretch gives a better rendition of imagery than no stretch at all. Percent Clip is a good default for imagery, as it cuts off a percentage of the highest and lowest pixel values to reduce the effect of outliers and applies a linear stretch to the remaining values.

8. Click the Stretch Type drop-down menu and choose Percent Clip.

   Next, you'll change the resampling type. The resampling type determines how pixels are displayed. The default type, Nearest Neighbor, closely retains the original pixel values, while other resampling techniques produce a smoother image by interpolating pixel values.

9. At the bottom of the Hallstatt Map view, for the map scale, type 1:200 and press Enter.

   

   At this scale, individual pixels are more clearly visible.

10. On the Raster Layer tab, in the Rendering group, click Resampling Type and choose Bilinear.

    

11. On the map, swipe to compare the Bilinear resampling type to the default Nearest Neighbor type.

    

    Though the effect is subtle, the Bilinear resampling type has a smoother transition between pixels. This is achieved by averaging the value of neighboring cells. In general, the Bilinear resampling method is most commonly used for imagery. You'll keep Bilinear as the resampling type.

    Note:

    The resampling rendering options only change the way the image is displayed. They do not change the actual image file permanently. You can learn more about the different types of stretch types and resampling types in the Image appearance documentation.

Change the band combination

Next, you'll change the band combination of the image. Currently, the band combination is Natural Color (red, green, and blue bands), which is closest to what is seen by the human eye. Since the image has a near-infrared band, you can create a Color Infrared (or false color) composite, in which the near-infrared band is displayed as red, the red band is displayed as green, and the green band is displayed as blue. The Color Infrared option highlights vegetation (in red) and water (in black).

1. On the ribbon, click the Map tab. In the Navigate group, click Bookmarks and choose Hallstatt.

   

   The map returns to the full extent of the image.

2. Click the Raster Layer tab. In the Rendering group, click Band Combination and choose Color Infrared.

   

   The band combination is changed. On the map, vegetation appears in red.

   

3. On the map, swipe to compare the Color Infrared and the default Natural Color band combinations.

   With Natural Color, it can be more difficult to distinguish whether there is any vegetation inside the village. With Color Infrared, the small vegetated areas between the buildings appear clearly in bright red.

   Note:

   If the imagery had more than four bands, you could create even more band combinations. Different band combinations highlight different features in the imagery. Try the Get started with imagery and Assess burn scars with satellite imagery tutorials for more examples.

   You're done comparing the two images, so you'll exit swipe mode.

4. On the ribbon, click the Map tab. In the Navigate group, click the Explore button.

   

   You'll also access the Symbology pane for the image to see which visualization options are available.

5. Click the Raster Layer tab. In the Rendering group, click the Symbology button.

   

   The Symbology pane appears. Most of the tasks you have performed to change the image's appearance, such as setting the stretch type, are also available from the Symbology pane.

   

6. Close the Symbology pane.

Save a layer file

Any changes you make to the appearance of a layer do not actually change the source data. Changes do not persist unless you save the project or save a layer file. Saving the project saves the state of the layer as you have edited it, but only within this project. Saving a layer file saves the layer appearance and allows that appearance to be reused in multiple projects. You'll create a layer file to persist the stretch, resampling, and band combinations you have applied to the layer.

1. In the Contents pane, right-click Hallstatt_Image.tif, point to Sharing, and choose Save As Layer File.

   

   The Save Layer File window appears. Using this window, you can browse to the location where you want to save the layer. The saved layer file has the .lyrx extension.

2. Expand Folders and click the Hallstatt_Imagery folder.

   

3. For Name, type Hallstatt_Color_Infrared.

   

4. Click Save.
5. In the Catalog pane, expand Folders and Hallstatt_Imagery. Right-click the Hallstatt_Color_Infrared layer file and choose Add To Current Map.

   Note:

   If the Catalog pane is not visible, click the View tab on the ribbon. In the Windows group, click Catalog Pane.

   

   The layer is added to the map. It has all the appearance parameters you set.

6. In the Contents pane, right-click the Hallstatt_Image.tif layer you added and choose Remove. Remove Hallstatt_Duplicate.tif.
7. On the Quick Access Toolbar, click the Save Project button. If prompted to save the project to the current version of ArcGIS Pro, click Yes.

   

In this tutorial, you inspected the properties of an imagery raster dataset. You also changed the appearance of the image and saved those changes to a layer file. The image is now ready for further exploration and analysis.

For more tutorials about working with imagery layers, try the Prepare imagery and raster data for analysis series.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Hallstatt data and imagery: State of Upper Austria
• World Topographic Map sources: Esri, TomTom, Garmin, FAO, NOAA, USGS, OpenStreetMap contributors, and the GIS User Community
• World Hillshade layer sources: Esri, Vantor, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community