Dr. Matthew Lee Profile

Dr. Matthew Lee

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Proceedings Article | 3 May 2016 Paper

Background adaptive division filtering for hand-held ground penetrating radar

Matthew Lee, Derek Anderson, John Ball, Julie White

Proceedings Volume 9823, 98230I (2016) https://doi.org/10.1117/12.2223294

KEYWORDS: Ground penetrating radar, Explosives, Deconvolution, Sensors, General packet radio service, Target detection, Radar, Detection and tracking algorithms, Metals, Statistical analysis

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The challenge in detecting explosive hazards is that there are multiple types of targets buried at different depths in a highlycluttered environment. A wide array of target and clutter signatures exist, which makes detection algorithm design difficult. Such explosive hazards are typically deployed in past and present war zones and they pose a grave threat to the safety of civilians and soldiers alike. This paper focuses on a new image enhancement technique for hand-held ground penetrating radar (GPR). Advantages of the proposed technique is it runs in real-time and it does not require the radar to remain at a constant distance from the ground. Herein, we evaluate the performance of the proposed technique using data collected from a U.S. Army test site, which includes targets with varying amounts of metal content, placement depths, clutter and times of day. Receiver operating characteristic (ROC) curve-based results are presented for the detection of shallow, medium and deeply buried targets. Preliminary results are very encouraging and they demonstrate the usefulness of the proposed filtering technique.

Proceedings Article | 28 May 2014 Paper

Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery

Matthew Lee, Yanbo Huang, Vijay Nandula, Krishna Reddy

Proceedings Volume 9108, 91080B (2014) https://doi.org/10.1117/12.2053072

KEYWORDS: Reflectivity, Hyperspectral imaging, Agriculture, Resistance, Image segmentation, Matrices, Cameras, Statistical analysis, Calibration, Spatial resolution

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Glyphosate based herbicide programs are most preferred in current row crop weed control practices. With the increased use of glyphosate, weeds, including Italian ryegrass (Lolium multiflorum), have developed resistance to glyphosate. The identification of glyphosate resistant weeds in crop fields is critical because they must be controlled before they reduce the crop yield. Conventionally, the method for the identification with whole plant or leaf segment/disc shikimate assays is tedious and labor-intensive. In this research, we investigated the use of high spatial resolution hyperspectral imagery to extract spectral curves derived from the whole plant of Italian ryegrass to determine if the plant is glyphosate resistant (GR) or glyphosate sensitive (GS), which provides a way for rapid, non-contact measurement for differentiation between GR and GS weeds for effective site-specific weed management. The data set consists of 226 greenhouse grown plants (119 GR, 107 GS), which were imaged at three and four weeks after emergence. In image preprocessing, the spectral curves are normalized to remove lighting artifacts caused by height variation in the plants. In image analysis, a subset of hyperspectral bands is chosen using a forward selection algorithm to optimize the area under the receiver operating characteristic (ROC) between GR and GS plants. Then, the dimensionality of selected bands is reduced using linear discriminant analysis (LDA). Finally, the maximum likelihood classification was conducted for plant sample differentiation. The results show that the overall classification accuracy is between 75% and 80% depending on the age of the plants. Further refinement of the described methodology is needed to correlate better with plant age.

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