The ability of radars to detect and classify targets has been greatly enhanced by maturation of the theory and practice of radar polarimetry. Although a substantial body of polarimetric measurements of natural surfaces has been accumulated over the last several decades at microwave frequencies, researchers have only recently begun to establish a similar data base at millimeter wavelengths. As an extension of this work, we have developed a fully polarimetric 225 GHz radar capable of remotely characterizing the average Mueller matrix of natural surfaces at ranges up to 1 km. We describe two incoherent measurement techniques, one which uses four transmit polarizations to uniquely specify the Mueller matrix, and a second using five or more transmit polarizations that reduces measurement errors. Documentation of the radar design is presented along with a description of the various procedures used to calibrate the receiver and transmitter subsystems. This radar was used to study the polarimetric behavior of a variety of natural surfaces, including decidous and coniferous trees, grass and sand. The ability to discriminate between planophil trees (horizontally oriented leaves) and erectophil trees (vertically oriented leaves) based on the fraction of polarized power is established for near grazing incidence. Dry sand was found to be the least polarized of all targets studied, bud became much more highly polarized when wet. These measurements show a substantial sensitivity to fine-scale surface features, and suggest the application of such a radar to class of remote sensing problems not addressed by other sensors.

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This dissertation develops new techniques to reduce computational complexity for hyperspectral remote sensing image analysis. Specific techniques are applied with regards to different applications of hyperspectral imagery, i.e., classification, target detection. The contribution of this dissertation can be summarized as follows. 1. A time efficient version combining multiple collaborative representations model is proposed for hyperspectral image classification. Collaborative representation (CR) can be implemented either with a dictionary containing training samples of all-classes or class-specific. A collaborative representation optimized classifier with Tikhonov regularization (CROCT) is proposed to avoid the redundant operations in all-class and class-specific versions. 2. An efficient probabilistic collaborative representation is presented for hyperspectral image classification. Its performance is evaluated on different types of spatial features of hyperspectral imagery including shape feature (i.e., extended multi-attribute feature), global feature (i.e., Gabor feature), and local feature (i.e., Local Binary Pattern). Experimental results show the probabilistic collaborative representation based classifier (PROCRC) has excellent performance in terms of both accuracy and computational cost compared with the original CRC and regularized versions of CRC. 3. Fast nonlinear classification and an explicit kernel approach are built for multispectral and hyperspectral imagery respectively to improve the kernel version of collaborative representation based algorithms. Experimental results show that using artificial bands generated from a simple band ratio function can yield better classification accuracy than the nonlinear kernel method and also reduce computational cost. In addition, the explicit kernel mapping approach can yield high accuracy as the original kernel versions of CR-based algorithms but with similarly low computational cost as in the original linear CRC classifiers. 4. Efficient band selection approaches are proposed for hyperspectral target detection. A maximum-sub-maximum ratio (MSR) metric has been applied for band selection, which can well gauge the target background separation. Efficient evolutionary searching methods such as particle swarm optimization and firefly algorithm are used in conjunction with maximum-sub-maximum ratio metric for band selection. Experimental results show that the proposed band selection approach can select a small band set while yielding similar detection performance compared with using all the original bands.

Recent technological advances have extended the use of some old technology such as the X-10 home automation. With the use of X-10 and the Internet, a remote control home is possible. One of the major energy consumption appliances in a house is the heating, ventilating, and air conditioning system. The main objective of this study is to explore the possibility of using X-10 and the Internet to serve as an energy saving system. A simulation is used to estimate the energy consumption.

Improving nitrogen (N) use efficiency is crucial for maximizing growth and yield in rice production. Tools to determine precise midseason N rates as well as knowledge of the effects of starter fertilizer N applications on rice growth and yield are lacking. Field experiments were conducted in 2007 and 2008 at the Delta Research and Extension Center to evaluate canopy reflectance as a means for determining yield and N nutrition at midseason and to assess agronomic effects and N recovery efficiency of starter fertilizer N. Measurable in-season parameters were significantly related to grain yield. Grain yield and measured parameters were related to canopy reflectance. The results of this study support the continued research of canopy reflectance for predicting N nutrition indicators and yield. Minimal growth responses were observed when starter N was applied to seedling rice. Nitrogen recovery increased significantly throughout the growing season; however, less than 20% recovery was obtained.

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