Image Management Technology-Topic 1: Database Import of original images

 

Have you ever encountered such troubles?

2. The storage location of massive data cannot be found;

2. Data at a certain time point cannot be found:

2. How fast is the processing speed of massive image data?

How should we deal with the above problems? This section uses massive image management technology to quickly create and store original image Landsat datasets, providing early data preparation for subsequent image applications.

1. View Original Image Data

Features of original images:

A) data obtained directly from the sensor

B)
Level 1 data (after Radiation Correction)

C)
Level 2 data (after geometric rough correction)

The above data can be obtained directly from the data agency. For specific data formats and storage methods, see Figure 1.1 and Figure 1.2.

Fig 1.1
RAW image data folder list

Fig 1.2
Original Image Data list

As shown in Figure 1.2, this data has eight bands, each of which exists independently. If you want to perform data management, you need to archive and store the data, the procedure is as follows. image Library.

2. Create an image library 2.1 create a geographic file database

In arccatolog, right-click the original image library in the folder->
Create-> file geographic database->
The name is Landsat.

Fig 2.1
Create a file-based geographic database "Landsat"

2.2 create an embedded Dataset

Right-click "Landsat panel. GDB"->
Create-> set datasets. Figure 2.2 is displayed.
In the dialog box, name the mosaic dataset landsatetm.

Fig 2.2
Create an embedded Dataset

Fig 2.3
An empty embedded dataset is successfully created.

2.3 original image data warehouse receiving

After the database is created, you need to import some existing original image data, such as Landsat image data, to the database. The procedure is as follows:

1)
Add Raster Data

Fig 2.4
Add Raster Data

2)
Add a grid to set parameters

Fig 2.5
Add Raster Data parameter settings

In the parameter Settings dialog box, select Landsat 7.

Fig 2.6
Auto Search adds Image Data Processing Process

2.4 view warehouse receiving result

1)
View warehouse receiving time

Fig 2.7
The original image data is 7.70 GB.

Fig 2.8
The storage time of original image data is 8 seconds.

2)
View warehouse receiving result

Fig 2.9
Data warehouse receiving result

From 2.9, we can see that the color difference between the original image and the whole scene is large in the stitching edge or the whole scene image. If you want to plot the thematic data, this display effect completely does not meet the actual needs. In order to better ensure the consistency of the entire data display, the dynamic results data needs to be color balanced. The specific operations are as follows;

3)
Color balancing

Before color balancing, we must first determine that statistics are performed on each band of the dynamic image data. If no pyramid or statistical value is constructed, we must first perform this operation, the procedure is as follows:

Right-click "landsatetm"->
Modify-> Create a project pyramid and a statistical value (for example, 2.10). The dialog box 2.11 is displayed.
.

Fig 2.10
Build the pyramid and statistical value

Fig 2.11
Create pyramid and Statistical Value dialog box

After building the pyramid and statistical value, right-click "landsatetm"->
Enhancement-> color balancing. Click this function. The following dialog box is displayed, as shown in Figure 2.12.
:

Fig 2.12
Color balancing parameter settings

Figure
2.13 Result Display after color balancing

4)
Image stretching

Right-click "landsatetm"->
Property-> function-> Insert "stretch function"-> OK; view the stretched image result, such as 2.14:

Fig 2.14
Image stretching Result Display

2.5 query warehouse image data

After the overall Landsat data is well managed through database creation and warehouse receiving, the most important concern of managers is how to quickly find the desired image data, next, right-click "outline"->
Open the attribute table and you will see some metadata attributes of the imported image data. For details, see 2.15.
.

Fig 2.15
Image Metadata Query Field