Abstract-Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a "best guess" map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded. As such, we show how, by using a Hopfield neural network, more accurate measures of land cover targets can be obtained compared with those determined using the proportion images alone. The Hopfield neural network used in this way represents a simple, robust, and efficient technique, and results suggest that it is a useful tool for identifying land cover targets from remotely sensed imagery at the subpixel scale.
11Logistic regression studies which assess landslide susceptibility are widely available in the literature. 12However, a global review of these studies to synthesise and compare the results does not exist. There 13 are currently no guidelines for selection of covariates to be used in logistic regression analysis and as 14 such, the covariates selected vary widely between studies. An inventory of significant covariates 15 associated with landsliding produced from the full set of such studies globally would be a useful aid to 16 the selection of covariates in future logistic regression studies. Thus, studies using logistic regression 17 for landslide susceptibility estimation published in the literature were collated and a database created 18 of the significant factors affecting the generation of landslides. The database records the paper the 19 data were taken from, the year of publication, the approximate longitude and latitude of the study 20 area, the trigger method (where appropriate), and the most dominant type of landslides occurring in 21 the study area. The significant and non-significant (at the 95% confidence level) covariates were 22 recorded, as well as their coefficient, statistical significance, and unit of measurement. The most 23 common statistically significant covariate used in landslide logistic regression was slope, followed by 24 aspect. The significant covariates related to landsliding varied for earthquake-induced landslides 25 compared to rainfall-induced landslides, and between landslide type. More importantly, the full range 26 of covariates used was identified along with their frequencies of inclusion. The analysis showed that 27 2 there needs to be more clarity and consistency in the methodology for selecting covariates for logistic 28 regression analysis and in the metrics included when presenting the results. Several recommendations 29 for future studies were given. 30 31
Abstract. The formulation of a generalized area-based confusion matrix for exploring the accuracy of area estimates is presented. The generalized confusion matrix is appropriate for both traditional classi cation algorithms and sub-pixel area estimation models. An error matrix, derived from the generalized confusion matrix, allows the accuracy of maps generated using area estimation models to be assessed quantitatively and compared to the accuracies obtained from traditional classi cation techniques. The application of this approach is demonstrated for an area estimation model applied to Landsat data of an urban area of the United Kingdom.
Abstract-Mixture modeling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve subpixel, area information. This paper compares a well-established technique, linear spectral mixture models (LSMM), with a much newer idea based on data selection, support vector machines (SVM). It is shown that the constrained least squares LSMM is equivalent to the linear SVM, which relies on proving that the LSMM algorithm possesses the "maximum margin" property. This in turn shows that the LSMM algorithm can be derived from the same optimality conditions as the linear SVM, which provides important insights about the role of the bias term and rank deficiency in the pure pixel matrix within the LSMM algorithm. It also highlights one of the main advantages for using the linear SVM algorithm in that it performs automatic "pure pixel" selection from a much larger database. In addition, extensions to the basic SVM algorithm allow the technique to be applied to data sets that exhibit spectral confusion (overlapping sets of pure pixels) and to data sets that have nonlinear mixture regions. Several illustrative examples, based on an area-labeled Landsat TM dataset, are used to demonstrate the potential of this approach.
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