Module 4 Lab: Spatial Enhancement, Multispectral Data, and Band Indices
Spatial Enhancement, MultispectralData, and Band Indices.
In this lab learned how to apply spatial enhancements, a preprocessing step preparing the imagery for use, how to view the properties of multispectral imagery, and create band indices.
Student Learning Outcomes:▪ Download and import satellite imagery
▪ Perform spatial enhancements in ArcMap and ERDAS
▪ Explore Image Histograms
▪ Operate the Inquire Cursor
▪ Interpret histogram data in images
▪ Utilize the “Help” menu effectively
▪ Identify features by interpreting digital data
▪ Create Spectral Band Indices
Task 1: Basic filters (in Imagine)
For task one we explored the various filters in Imagine including the highpass, lowpass and sharpen filters as well as various tools and best use cases for each.
.png)
Task 2: Other filters
For this task we explored the Focal statistics tool including the mean and range statistic types including edge detection filters.
.png)
Exercise 3: Image Histograms
For exercise three we explored the various ways to use histograms to interperate and identify features.
.png)
Exercise 7: Deliverable Assignment
Instructions: "You will be using these methods to find three features, and then create a map displaying each of them. Certain features have definite and relatively constant reflectance properties, and therefore will always fall in the same place on the histogram. Sometimes, a feature can be identified by its reflectance in just one bandwidth. In most cases, however, it is necessary to examine a feature’s reflectance across a combination of several bandwidths."
Background:1. Examine the histogram for shapes and patterns in the data.2. Visually examine the image as grayscale for light or dark shapes and patterns.3. Visually examine the image as multispectral, changing the band combinations to make certainfeatures stand out.4. Use the Inquire Cursor to find the exact brightness value of a particular area.
-
.jpg)
Feature 1: To identify this feature I used the histogram to identify that the feature depicted must be large and dark. I then visually inspected the panchromatic and multispectral views to identify that the darkest area represented is the sound. From there I used the inquire cursor to cross reference the values of the sound with the histogram to confirm. I used the Near infrared color composite to make the sound stand out from the surrounding features.
.jpg)
Feature 2: To identify this feature I used the histogram to identify that the feature depicted must be small and bright. I then visually inspected the panchromatic and multispectral views to identify that the brightest area represented is the snow covered mountains. From there I used the inquire cursor to cross reference the values of the sound with the histogram to confirm. I used the True color spectrum to denote the mountain tops.
Feature 3: To identify this feature I visually inspected the panchromatic and multispectral views to identify areas of the sound which have appeared brighter than the rest within certain bands. I used the Esri recommended Bathymetric spectrum to denote the sound.
In this lab learned how to apply spatial enhancements, a preprocessing step preparing the imagery for use, how to view the properties of multispectral imagery, and create band indices.
Student Learning Outcomes:
▪ Download and import satellite imagery
▪ Perform spatial enhancements in ArcMap and ERDAS
▪ Explore Image Histograms
▪ Operate the Inquire Cursor
▪ Interpret histogram data in images
▪ Utilize the “Help” menu effectively
▪ Identify features by interpreting digital data
▪ Create Spectral Band Indices
▪ Perform spatial enhancements in ArcMap and ERDAS
▪ Explore Image Histograms
▪ Operate the Inquire Cursor
▪ Interpret histogram data in images
▪ Utilize the “Help” menu effectively
▪ Identify features by interpreting digital data
▪ Create Spectral Band Indices
Task 1: Basic filters (in Imagine)
For task one we explored the various filters in Imagine including the highpass, lowpass and sharpen filters as well as various tools and best use cases for each.
.png)
Task 2: Other filters
For this task we explored the Focal statistics tool including the mean and range statistic types including edge detection filters.
.png)
Exercise 3: Image Histograms
For exercise three we explored the various ways to use histograms to interperate and identify features.
.png)
Exercise 7: Deliverable Assignment
Instructions: "You will be using these methods to find three features, and then create a map displaying each of them. Certain features have definite and relatively constant reflectance properties, and therefore will always fall in the same place on the histogram. Sometimes, a feature can be identified by its reflectance in just one bandwidth. In most cases, however, it is necessary to examine a feature’s reflectance across a combination of several bandwidths."
Background:
1. Examine the histogram for shapes and patterns in the data.
2. Visually examine the image as grayscale for light or dark shapes and patterns.
3. Visually examine the image as multispectral, changing the band combinations to make certain
features stand out.
4. Use the Inquire Cursor to find the exact brightness value of a particular area.
-
.jpg)
Feature 1: To identify this feature I used the histogram to identify that the feature depicted must be large and dark. I then visually inspected the panchromatic and multispectral views to identify that the darkest area represented is the sound. From there I used the inquire cursor to cross reference the values of the sound with the histogram to confirm. I used the Near infrared color composite to make the sound stand out from the surrounding features.
.jpg)
Feature 2: To identify this feature I used the histogram to identify that the feature depicted must be small and bright. I then visually inspected the panchromatic and multispectral views to identify that the brightest area represented is the snow covered mountains. From there I used the inquire cursor to cross reference the values of the sound with the histogram to confirm. I used the True color spectrum to denote the mountain tops.
Feature 3: To identify this feature I visually inspected the panchromatic and multispectral views to identify areas of the sound which have appeared brighter than the rest within certain bands. I used the Esri recommended Bathymetric spectrum to denote the sound.
Comments
Post a Comment