GIS4035 - Module 4 - Image Preprocessing : Spatial & Spectral Enhancements and Band Indices

 

False color infrared composite (Bands 4, 3, 2) of the Landsat TM scene
used in this week’s lab. (For demonstration only)

This week’s material focused on the foundations of image preprocessing and multispectral interpretation in remote sensing. The readings introduced the major types of corrections applied to satellite imagery (radiometric, atmospheric, and topographic), which are essential for improving data quality before analysis. We also learned how spatial enhancement filters, such as low-pass, high-pass, sharpen, and Fourier transforms, can smooth noise, enhance edges, or reveal patterns caused by the sensor.

Another key topic was interpreting image histograms and LUT adjustments. Understanding where spikes fall in the histogram and how breakpoints stretch contrast helped connect numerical brightness values to real-world features. This directly tied into multispectral interpretation, where we examined how land cover types such as water, vegetation, soil, and snow behave differently across visible, NIR, and SWIR wavelengths. Spectral indices like NDVI provided additional insight into surface characteristics.

The lab applied these concepts through a series of spatial filters, histogram exercises, and multispectral analysis. Exercise 7 brought everything together by requiring us to identify three features in the Landsat image using histogram spikes, grayscale inspection, band combinations, and pixel-level DN sampling.
Below are my detailed explanations for each feature identified in Exercise 7.

In Band 4, the near-infrared (NIR) band, the histogram shows a clear spike between DN 12 and 18, with DN 14 occurring 1.24 million times. Because this spike sits on the far left side of the histogram, it represents something that reflects almost no NIR energy. In grayscale, these values appear very dark, which is a strong indicator of water.

Using the Inquire Cursor, I confirmed this by sampling several pixels in the middle of an open water body, all of which returned DN values around 14. This links the histogram spike directly to water in the scene.

To make this feature stand out visually, I used a 4-3-2 false-color composite (NIR, red, green). Healthy vegetation appears bright red, urban areas and soil show up in cyan or light green, and open water stays very dark because of its low NIR reflectance. This combination made the water features extremely easy to distinguish from the surrounding landscape.

For Feature 2, the histograms for Layers 1–4 each show a small spike around DN ≈ 200, indicating a feature that is very bright in the visible and NIR bands. Layers 5 and 6 (the SWIR bands) show a large spike at DN ≈ 9–11, indicating that the same feature is very dark in SWIR. Using the Inquire Cursor on the bright white areas on the mountain tops, I found pixels with DN values of 190–210 in Layers 1–4 and 9–11 in Layers 5 and 6. These pixels correspond to snow/ice at high elevations. 

Ice and snow are highly reflective across the visible and NIR spectral ranges and strongly absorb in the SWIR bands. This explains why the snow-covered mountains are bright in Bands 1–4 and much darker in the SWIR bands (TM Bands 5 and 7, represented by Layers 5 and 6 in this image).

For Example 2 (snow/ice), I used a 5-4-3 false-color composite (SWIR1–NIR–Red). Snow and ice are bright in the visible/NIR bands but dark in SWIR, so they appear as bright cyan/blue-green in this combination, clearly separated from surrounding forest and rock.

For Feature 3, I focused on the waters of Grays Harbor, a large estuarine bay on the Pacific coast in Washington State, which appear noticeably brighter than the rest of the water in our study image. Using the Inquire Cursor across different layers, I found that the harbor consistently shows higher DN values in Layers 1–3, the visible bands. In the area I examined, DN values increased by roughly 8 to 12 points compared to the darker water elsewhere in the scene.

In Layer 4 (NIR), the brightness only increases by a small amount, and Layers 5 and 6 (SWIR) remain essentially unchanged.

To compare the bay with the rest of the water in the extent, I created a subset containing only the bay itself and examined its histogram to look at the mean values. The brighter reflectance in Grays Harbor is most likely caused by suspended sediment, tidal mixing, or a combination of the two, which would naturally brighten water in the visible spectrum without affecting NIR and SWIR in the same way.

I also considered other locations, including a small patch in the North Bay where false color IR shows vegetation or algae, and an area of swirling water near Aberdeen. Both were interesting, but neither matched the spectral pattern as clearly as the broader bay did.

The best band combination for observing these differences was True Color (3-2-1), which made the brightness variations in the bay stand out clearly.