Aerial Photography and Remote Sensing - Module Lab 5 - Unsupervised & Supervised Classification

Hello Everyone!

It's hard to believe, but this week's lab is the final lab of this awesome remote sensing class before I start my final project! With that being said, this week's lab was a great way to end the labs for the semester! This week we covered supervised and unsupervised classification. Unsupervised classification essentially gives the user the power to select how the image is classified using pixel values that match across spectral classes. These classes can then be changed individually so when you change the class, all pixel values in that class change. Supervised classification essentially uses the image analyst to supervise the selection and creation of spectral classes within the program. By defining the areas of classification, it then can classify the image based on a handful of 'seeded' areas.

For this week's deliverable, I was tasked with using supervised classification to classify the landcover/use of Germantown, Maryland. For this exercise, I created 8 seeded areas of interest classification signatures that would serve as the classification seeds for my image. I then chose a band combination (within the map title) that caused the least amount of spectral confusion and then recoded my values. Once my image was complete, I added the new class names and area of each class in acres. Additionally, I added and created a Distance map which shows where areas of classification are likely incorrect. The brighter the area, the higher the chance that the classification of that feature was wrong.

I am really glad that everyone has been following me through this incredible journey in the world of remote sensing and aerial photography, it is truly one of the coolest fields of GIS and I would love to work on this for my capstone research!

~Map On!

Aerial Photography and Remote Sensing - Module Lab 4 - Spatial Enhancement, Multispectral Data, and Band Indices

Hello Everyone!

For this week's lab, I worked in ERDAS Imagine to manipulate and enhance various remote sensed images. This week's lab touched on various forms of image manipulation from band layer combinations of Red, Green, Blue which changes the image feature colors, the sharpening, and refining of edges in imagery, and identifying features based on their histogram and band information. Below you can see the example of one of the three features I was to identify. Each of the three features had specific values for pixel values, histograms, and criteria that make them unique. One of my examples can be seen below:

For this map, the feature I was looking for was described as a feature causing a small spike in pixel values within Layers 1-4 around 200 and a large spike between pixel values 9 and 11 in Layer 5 and 6. To determine this feature I first assessed the histogram for each of the 6 layers and then determined the spike areas. I then used the ERDAS Identifier tool to look at the suspected snow area to see if it met both pixel value criteria. When the feature was confirmed as correct, I chose the following band combination to help distinguish the snow cover in the image, Red: Layer 6, Green: Layer 5, and Blue: Layer 3. This causes the snow to appear blue and distinguish itself from the surrounding features. 

~Map On!

Aerial Photography and Remote Sensing - Module Lab 3 - Intro to ERDAS Imagine and Digital Data

Hello Everyone!

This week's lab was all about using ERDAS Imagine which is essentially an image processing software that allows users to manipulate and view various types of remotely sensed data and other aerial imagery. Additionally, ERDAS supports vector data. ERDAS grants a user to view multiple sensed images at once, modify their color spectrum and even layer various types of data in one frame. Through ERDAS this week we took a subset snapshot of a remotely sensed image of Washington State. We then pulled this image of types of landcover into ArcGIS pro and created a map set. Through ERDAS I was able to see the category names of each landcover type for each pixel and calculate the area that each type of land cover took up within the subset image. My map can be seen below: 

As you can see, there are 7 (6 present) types of class area land covers within my subset map image. Within the legend, you can see the acreages of those class areas calculated. For this class, we will be using ERDAS more which is a very powerful tool that I am looking forward to using!

~Map On!

Aerial Photography and Remote Sensing - Module Lab 2 - Land/Land Cover Classification and Ground Truth

Hello Everyone!

This week's lab was focused on Land Use and Land Cover Classification and Ground Truthing. Land Use can be defined as the way humans use the landscape. This landscape usage can range from residential to commercial to agriculture or industrial. Land Use is not the easiest to decipher from a satellite image. Land Cover, on the other hand, is the biophysical description of the surface of the earth. Land cover can range from water to forests or wetlands. These types of features are much easier to identify from a satellite image. For this lab, I was tasked with creating a Land Use and Land Cover map from a satellite image of a portion of Pascagoula, Mississippi. For every unique feature on the image at a working small scale, I digitized and created polygon boundaries for each of the various types of land cover and land uses. The map I created can be seen below:

For my map, I ended up with 12 unique land use and land cover classes. For the second part of my lab, I had to ground truth 30 test point locations and create an accuracy statement. Overall I had a visual interpretation accuracy value of 80% which means 80% of my deduced land cover and land use guesses were correct and my map has 80% accuracy. See you next week!

~Map On!