Research abstract - Remote Sensing Lab

Problem Statement/Abstract
The overall goal of my project is to employ physical measurement methods and image analysis techniques on soil samples to validate known measurements such as density and grain size. Ultimately, the results obtained by this research could be used to measure the trafficability, stability, and compaction of a surface. There are many known methods to measure particle size distribution (PSD) within a sample, including wet and dry sieving, laser diffraction, X-ray attenuation, manual and automated image analysis techniques, etc... In this research project, we will be focusing on the different sieving methods in addition to both manual and automated image analysis. More specifically, I will be comparing the dry sieving method with imaging techniques (manual and automated). Dry sieving is carried out using an analytical sieve shaker and measures the PSD based on the percentages of the weight retained within the different sieves. Dry sieving segregates grains by the length of the smallest and intermediate grain axes. In general, studies found that dry sieving of soil samples resulted in larger particles being produced, mainly due to the clumping of smaller particles caused by the drying process. I will also be looking at and experimenting with image analysis techniques over the course of this internship. There are two types of image analysis as mentioned earlier, manual and automated. With manual image analysis, I will be using image J and recording the lengths/sizes of each individual particle within an image. On the other hand, automated image analysis involves use of the wavelet method, which quantifies both spectral and spatial information from the sediment image simultaneously. The wavelet method is calibration-free and a transferable measure of grain size distribution (GSD). Additionally, automated-processing techniques can uncover physical processes/phenomena that traditional sampling (i.e. sieving) cannot. In the end, I will compare the data gathered from my experiments using the different methods (dry sieving and manual and automated image analysis) to determine which technique is the most suitable for remote sensing applications.



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