Supervised Image Classification - Germantown, Maryland

The fifth module for Remote Sensing and Photo Interpretation introduced Supervised and Unsupervised Digital Image Classification techniques. These are automated processes for converting a spectral class, a group or cluster of spectrally similar pixels, into an information class, i.e. land use/land cover class of interest.

Using multi-spectral data and spectral pattern recognition techniques, the algorithm may take many spectral classes to describe a single information class. Similarly one spectral class may represent more than one information class.

Unsupervised classification uses a form of clustering algorithm to determine what land cover type each pixel most matches. The tendency of spectral data is that pixels of one land cover class tend to cluster together. Within the Lab using ERDAS Imagine, I inputted a high resolution aerial photograph of the UWF campus and used Unsupervised Classification processing with the number of classes set to 50. Maximum Iterations and Convergence Threshold were set to prevent the program from running an infinite loop, and with a threshold resulting in a 95% level of confidence for the classification of pixels.

Eventually through reclassification, the 50 classes generated from ERDAS were pared down to just five. Using visual interpretation techniques, pixels were identified and assigned into classes for trees, grass, buildings/road, shadows and a mixed classification group. The mixed classification included pixels associated with more than one type of land use/land cover.

The final output after reclassifying the 50 spectral classes into five

Supervised image classification uses one of two approaches. The first is Signature Collection, where an analyst uses prior knowledge and visual techniques to identify different types of LULC to establish training sites. The second grows a polygon from a Seed pixel, where an algorithm develops training sites which tries to decrease n-class variability (N being the variable number of bands) such as by setting variance, total number of pixels and values of adjacent pixels. This is similar to the Magic Wand tool in Photoshop, which selects similar pixels across a selected area.

The supplied imagery was from Landsat TM4 of Germantown, Maryland with a low spatial resolution. The image was likely from the late 1980s or between 1990 and 1992 based upon visible construction to the south expanding Interstate 270 to four roadways.

The lab instructions provided LULC classifications for 12 preselected sites based upon their coordinates. As an analyst, I was tasked to create an AOI (Area of Interest) polygon representative of all pixels falling within the LULC specified. This proved to be quite tedious, as the spectral class of several pixels represented multiple information classes.

The AOI polygons were used to create a Spectral Signature file that ERDAS Imagine applies to an automated process for classifying all pixels within the imagery. AOI areas included urban/residential, grasses, two forest types, fallow fields and agricultural areas. Upon completion of populating the signature file, the imagery was reclassified using the Parametric Distance Approach and Maximum Likelihood classification method.

The first attempt resulted in over 10,000 acres of the imagery classified as urban/residential. Comparing the imagery with aerial photography of Germantown, MD from 1993 on Google Earth, this was clearly over exaggerated. The recoding process resulted in many agricultural tracts being misclassified as urban.

First attempt at supervised image classification for Germantown, MD LULC

Opting to start over on the AOI creation, I narrowed down my pixel areas for most of the preselected training sites. Recoding it resulted in large areas of fallow fields in place of agriculture. Returning to the signature file created, I added several new training sites based upon the distance image and visual interpretation. These mostly focused on better identifying agricultural areas.

The result was a more accurate classification of the imagery when it came to both urban/residential and agriculture. While there were still areas of farm land misclassified as urban, the percentage was vastly lower than my previous attempts.

Final output of Germantown, MD LULC using Supervised Image Classification

Multispectral Data Analysis - Olympic Pensinula, Washington

This week's Remote Sensing and Photo Interpretation lectures and lab assignment introduced a myriad of information related to image preprocessing. Functionality and various atmospheric correction techniques were discussed, followed by in depth look at several vegetation indices. The textbook provided great detail of the spectral characteristics of vegetation, including how visible light interacts with tree leaves and canopies.

The lab introduced data acquisition and the USGS Global Visualization Viewer (GloVis). There are a great many options with the web site, and we only scratched the surface with a single download of Landsat 4-5 TM data for the Pensacola area from 2011.

Next was an overview of Spatial Enhancement of raster data and a number of filters. Among other tasks, filters such as Fourier transform can be used to repair data such as with banding while convolution filtering can generalize data and sharpen data. Histograms can be utilized to omit unneeded data from analysis.

Working in ERDAS Imagine, I implemented a low pass filter on panchromatic imagery. This smooths out an image and is similar to the blur filter in Photoshop. It consolidates pixels within a 3x3, 5x5 or other kernal into an average, so local variation is reduced and "noise" removed. More specifically it takes the average digital number (DN) value of all cells within the kernal, and applies it the central kernal.

Unedited Panchromatic Imagery - Band 8 with wavelengths of 0.52-0.90 micrometers

So some of the smaller details found across the rows and columns of pixels are reduced or eliminated while effects on larger scale features are minimal. When viewing imagery more broadly, this can aid in interpreting large-scale patterns. It also can correct for erroneous pixels, such as a stuck pixel in digital photography, where bad data or missing data may have occurred with the initial image generation.

Low Pass Filter image processing on Panchromatic Imagery

Shifting to high pass filter on the same panchromatic imagery, this function accents features with edges while also enhancing linear features. It accentuates differences between one cell's DN and its neighboring cells. High levels of change for DN values from rows and columns of the imagery become more apparent.

High Pass Filter image processing on Panchromatic Imagery

Also referred to as edge-enhancement, the high pass filter highlights boundaries between features, such as the edge of a forest stand by farmland or a shoreline. Sharpening the edges between objects aids in seeing small scale differences while reducing broad scale patterns. I equated this with the sharpen tool in Photoshop.

Convolution Filter with kernals of 3x3 Sharpen

Within ArcGIS Pro, I used the Focal Statistics tool. Similar to Block Statistics, which calculates a new single cell value for every cell within a kernal, Focal Statistics calculates new values for every cell in the raster imagery.

There are several Statistic type options within the Focal Statistics geoprocessing tool. Using the Mean statistic type, the larger kernal resulted in each new cell value being the average of the larger area, resulting in a more generalized image.

ArcGIS Pro Geoprocessing tool Focal Statistics with a 7x7 kernal and Mean as the filter

The Range statistic type, commonly referred to as Edge Detect, generates a new call value that reflects the difference or Spatial Frequency in DN values between adjacent cells. High DN values are assigned for pixels with high spatial frequencies, where the maximum value in the kernal is subtracted by the minimum value in the kernal. This tool can be used to reveal edges or borders between different types of features.

ArcGIS Pro Geoprocessing tool Focal Statistics with a 3x3 kernal and Range as the filter

The last image preprocessing tool used this week was Image Histograms. The Histogram is a statistical graph comprising the total range of DN values, with spikes referencing high amounts of pixels (areas in the raster) that are outliers as compared to the homogeneous ranges for the bulk of the remaining pixels. The spikes can represent areas within the overall imagery where brightness values are very dark or very light as compared to the rest.

Adjusting the Radiometry in ERDAS Imagine using the Histogram breakpoints allows us to exclude pixel ranges outside of them from the imagery. This narrows down the brightness scale, with dark areas to the left and right areas to the right of the overall Histogram. Performing this operation can enhance the visualization of data.

When we adjust Levels in Photoshop, the Histogram is part of that filter. When a photo is visually too dark or blown out with light, adjusting the Levels can drop those extreme DN's from the visual presentation, improving the look. Having edited photos for 23 years on AARoads, I know better understand how this filter works!

Photo before preprocessing, large DN range beyond the histogram spike

Processing the photo with the Levels filter using histogram, break points DN's adjusted to 8 and 211

The following maps covering the Olympic Peninsula in Washington State from Seattle west to the Olympic Mountains. The lab tasks involved identifying features based upon three separate radiometric criteria. The data was from the Landsat 5 Thematic Mapper (TM) satellite, a multispectral sensor assigning the first seen spectral bands to the following spectral regions with wavelength frequencies in micrometers:

Band 1 / Blue visible light - 0.45-0.52

Band 2 / Green visible light - 0.52-0.60

Band 3 / Red visible light 0.63-0.69

Band 4 / Near Infrared (NIR) 0.76-0.90

Band 5 / Shortwave Infrared (SWIR) 1 - 1.55-1.75

Band 6 / Thermal 10.40-12.50

Band 7 / Shortwave Infrared (SWIR) 2 - 2.08-2.35

Once a feature is identified, the lab specified using the Create Subset tool to extract a corresponding area. Bringing the subset into ArcGIS Pro, colors for the spectral bands were assigned to enhance the visualization of the features to be identified.

The first map produced shows Seattle and areas of deep, clear water with low DN values. Using False Color IR imagery, where red is assigned to Near Infrared (NIR), the darker pixels with Puget Sound, Lake Washington and Port Orchard are easily distinguishable.

The criteria for the second feature to identify focused on the histogram of the Landsat 5TM image where a spike in Bands 1-4 corresponded with pixels around a DM value of 200. Additionally the criteria also called for areas where the histogram for Bands 5 and 6 spiked between DM values of 9 and 11. This referenced areas of snow and glacial ice high in the Olympic Mountains.

Went with a False Natural Color map for the data where it clearly shows snow/ice as light blue, a result of the high radiant reflectance of red, green and blue visible light, which is visibly as the color white. Areas adjacent to the glaciers and snow pack reflect the Mid-Infrared and the NIR shows the abundant forest cover on the mountains.

Last to identify for Lab 4 were areas of water where visible light (bands 1-3) were brighter than normal. NIR reflectance is also slightly higher, but DM values of bands 5-6 remained unchanged. EMR penetrates deeper in clear water, resulting in higher absorption rates and low reflectance. Within shallower and turbid water, the presence of sediment and other particles in the water causes more EMR to reflect, giving the brighter appearance.

Within the Puget Sound area, brighter surface water is associated with mud flats or where streams dump into the larger bays.

Introduction to ERDAS Image - Washington Forest Thematic Data

The third lab assignment for Remote Sensing/Photo Interpretation introduces ERDAS (Earth Resources Data Analysis System) Image. The first part of the lab provided a basic overview of the program, initially with some rudimentary tasks involving Advanced Very High Resolution Radiometer (AVHRR) imagery and a LANDSAT Thematic Mapper (TM) satellite image of forestland in Washington State.

Working with the data from the Olympic Peninsula in Washington, learned how to add an area column to the attributes table that calculates the area in hectares for each land classification. Focusing on a smaller area within the overall imagery, then used the Inquire option to extract a subset image for closer analysis. Next needed to recalculate the area for the subset image prior to outputting the file for ArcGIS Pro.

Within ArcGIS Pro, symbolized the subset image and then created a final layout showing the thematic classification with area size statistics: