Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

Variable grouping in multivariate time series via correlation

The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho

Building safe software

Murphy is a set of techniques and tools under investigation for their potential in enhancing the safety of software. This paper describes some of the work which has been done and some which is planned

Neural nets - their use and abuse for small data sets

Neural nets can be used for non-linear classification and regression models. They have a big advantage over conventional statistical tools in that it is not necessary to assume any mathematical form for the functional relationship between the variables. However, they also have a few associated problems chief of which are probably the risk of over-parametrization in the absence of P-values, the lack of appropriate diagnostic tools and the difficulties associated with model interpretation. The first of these problems is particularly important in the case of small data sets. These problems are investigated in the context of real market research data involving non-linear regression and discriminant analysis. In all cases we compare the results of the non-linear neural net models with those of conventional linear statistical methods. Our conclusion is that the theory and software for neural networks has some way to go before the above problems will be solved