Remote Sensing: Module 8 - Thermal Imagery

     In continuing with digital image processing, thermal imagery is introduced. Thermal remote sensing is unlike other forms of remote sensing in that sensors record emitted energy as opposed to reflected energy. A general rule of thumb when examining thermal data is that good absorbers are good emitters of thermal radiation while good reflectors are poor emitters of thermal radiation. Thermal characteristics will also vary by time of day and season which is taken into consideration when planning a study or recording data for a site.

     Thermal imagery has a coarser spatial resolution than other bands because it has a larger instantaneous field of view. This came into play during the lab as there were two choices of imagery for analysis. I initially chose a different image than I ultimately used because I thought it had obvious features (like recently irrigated fields) to identify using the thermal layer (which was the goal of the assignment). The lab, however, wanted the feature highlighted in the map and when I attempted to make the map the irrigated field was too pixelated for useful visual analysis. Thus, the map deliverable below. It shows an image with a better spatial resolution (and I chose a large feature to eliminate scale issues altogether). On the left side of the map is the feature visualized in a band combination (4, 5, 1) that highlights its extent. On the right side of the map is the thermal layer (Landsat ETM+, Band 6) displayed using a red, yellow, and blue gradient. 

     The thermal image shows a distinct spot near the coast that is much warmer than the surrounding water (also note the red areas as evidence of cultural activity/urbanization). When it is visualized in the multispectral image it shows as a bright red swath along the coast. The red is a result of reflected near infrared energy. This could indicate some photosynthetic activity or agricultural runoff. A combination of histrogram analysis/manipulation, viewing the image in greyscale for each band, and the inquire tool (in Imagine) were used to analyze this image.

Map 1: An Exercise in Thermal Image Interpretation

Remote Sensing: Module 7 - Image Preprocessing Part 2

     This assignment continues examining image processing techniques by way of multispectral analyses. Different band combinations can be used to tease out information that may be missed when viewing an image in true color. In other instances, true color is the best option for viewing a given feature. What band combination is best is ultimately determined by what feature is under examination.
     Three features were described in our lab assignment and we were tasked with identifying them based on the given criteria. Histogram spikes and trends helped indicate whether a feature has dark or bright pixel values. From there, examining greyscale versions of each band in conjunction with multispectral versions of the image aid in visually assessing trends and confirming patterns in the histogram. In ERDAS Imagine, the Inquire Cursor is invaluable. It provides information about a pixel for each band including its frequency and LUT value. The Inquire Cursor helped confirm the features described.
    Below are the map deliverables for this assignment. Each map shows one identified feature displayed in a band combination that highlights that aspect. An inset map is included in true color to provide a true-to-life context. Without giving away too much detail, the first feature is a water body as the pixel characteristics matched the histogram characteristics provided for a certain layer. The second and third features are snow atop a mountain peak and sediment in water, respectively. The pixel values also fit the given description. Band combinations are noted on each map with a brief note on why that combination is used. t

Feature 1

Map 1 Feature Identification Exercise Using Multispectral Analysis -- Water Bodies 

Feature 2

Map 2 Feature Identification Exercise Using Multispectral Analysis -- Snow

Feature 3

Map 3 Feature Identification Exercise Using Multispectral Analysis -- Variation in Water

Remote Sensing: Module 6 - Image Preprocessing Part 1

     This modules covers spatial enhancements and image preprocessing. The exercise introduces the USGS Global Visualization Viewer where satellite and aerial data can be browsed and downloaded. These satellite images can then be imported into ERDAS Imagine for analysis. Some of the Landsat imagery exhibits striping due to a sensor malfunction and in this lab we attempted to correct for this phenomenon with spatial enhancements. These can be performed in either ArcMap or Imagine. Edge detection, Fourier transformations, and other techniques help in getting the most out of remotely sensed data.
     Two maps below show my attempts at image enhancement. The first one is the map deliverable for the exercise. I used the Focal Analysis tool in Imagine iteratively. That is, I performed a focal analysis calculating the mean of a 3x3 kernel (excluding 0 values from the calculations) and applying those values to pixels with a value of 0. Performing this analysis to each end product of the previous analysis (using the same exact settings) results in gap closure. The image, however, is not perfect and there is a disconnect between the gap filled areas and the original data. causing it too look like an improper join. This image is a composite between the original banded image and the modified image. Other methods attempt to fill missing data by using Fourier transformations in conjunction with other filters (low pass, high pass, range/edge detect, sharpening).

Map 1: Image Enhancement Exercise Using Landsat Satellite Imagery

     The second map I created shows some of the end products of combinations of filter effects and enhancements including focal statistics, histogram adjustments, and altering raster display settings. The top left image is the original, unaltered image with gaps. The top right is the image I used in my final map above. The bottom left and bottom right images are composites using the banded image. The bottom left image uses a Fourier transformed, sharpened image. The bottom right image was made by using focal statistics (maximum) and a Fourier transformation. 

Map 2: Various Attempts at Image Enhancement Using Different Methods

     While you can perform these enhancements in ArcGIS, I predominately used ERDAS Imagine. Both programs are great but I like that I could open four views in Imagine and compare them simultaneously. Imagine also lets you preview the alterations which I found helpful.