This thesis presents an automatic soil texture classification system using hyperspectral soil signals and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, which use images for training and testing. Although using image-based algorithms is a straightforward way to conduct soil texture classification, our research shows that it does not provide reliable and consistent results. Rather, we develop a novel system using hyperspectral soil textures, better known as hyperspectral soil signals, which provide rich information and intrinsic properties about soil textures. Hyperspectral soil textures, in their very nature, are nonstationary and time-varying. Therefore, the wavelet transform, which is proven to be successful in such applications, is incorporated. In this study, we incorporate two wavelet-domain statistical models, namely, the maximum likelihood (ML) and the hidden Markov model (HMM) for the classification task. Experimental results show that this method is reliable and robust. It is also more effective and efficient in terms of practical implementation than the traditional image-based methods.

Purpose - This thesis addresses the lack of research on Performance Management (PM) in Hidden Champions (HCs) due to development and implementation of a HC tailored PM system (PMS) in Action Research Design (AR). A HC tailored strategy map and PMS framework were elaborated to improve the financial and non-financial performance of the HC. Design/methodology/approach - A longitudinal case study with two AR cycles were performed to develop the HC tailored strategy map and framework for the PMS including their implementation using mixed methods data collection. A validated HC tailored strategy map and framework for the PMS implementation are presented. Findings - Findings show significantly increased financial and non-financial performance of the HC. The revenue of the case company increased by 11.6%, the return on revenue increased 3% up to 17%, customer satisfaction grew, throughput times shortened and leadership competencies improved. Findings further show that AR is an effective procedure for HC tailored PMS design and implementation. Research limitations and practical implications - The findings are based on one case study and needs to be tested in other HCs. Findings should be of interest to scholars in the field of PM, as well as practitioners improving financial and non-financial performance. Originality/value -This thesis contributes to knowledge of PM in HCs, and AR, as it provides a validated HC strategy map and framework for HC tailored PMS implementation in a HC. It provides an approach for practitioners to improve financial and non-financial performance in HCs.

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