Working with Landsat is easy using ArcGIS to enhance, analyze, and correct Landsat data.
ArcGIS provides two specialized processing capabilities for Landsat—the apparent reflectance function and the Kauth Thomas transformation. Built-in access to this technology makes it easier to understand the information contained in Landsat imagery.
The apparent reflectance function in ArcGIS removes most of the radiometric distortions in Landsat imagery, resulting in more accurate analysis and better display of imagery. The function adjusts the images to a theoretically common illumination condition, resulting in less variation between scenes from different dates and from different sensors. This is useful for image classification, color balancing, and mosaicking.
Tasseled cap services
ArcGIS has included support for the Kauth Thomas transformation by integrating Tasseled Cap Services in the core products. The Tasseled-Cap transformation is a conversion of the original bands of an image into a new set of bands with defined interpretations that are useful for vegetation monitoring and change mapping. The three most important components of the transformation are known as Brightness, Greenness, and Wetness.
Correction and enhancement of Landsat Imagery
Landsat provides one of the best sources for understanding earth changes over the last 4 decades. Over time, technology has improved to enhance existing Landsat data and fix problems inherent in the data due to sensor malfunction. ArcGIS provides tools to automatically correct some of the anomalies in Landsat.
Pseudo color MSS (1975 Epoch)
The Landsat 1-4 MSS sensors did not have a blue band which is helpful for bathymetric, water quality, and vegetation mapping applications. In ArcGIS, raster functions can be applied on the fly to simulate the blue band, thereby producing a pseudo-colored rendition of a natural color product.
In Landsat-7 data after 2003, you may be familiar with the Scan Line Corrector (SLC) problem which results in data gaps across the scene. ArcGIS has the ability to destripe those scenes and make them more visually appealing by interpolating data across the data gaps. This results in enhanced visualization when comparing scenes from multiple epochs. The same techniques are effective in compensating for other image data anomalies such as dropped lines, missing data, and detector malfunction.