Landcover Mapping and Change Detection of Geographical Area on the Map
01 Feb 2017-pp 188-190
TL;DR: In this paper, a simple, easy to implement method for calculating the geographical area of a place on the map using the Shoelace Algorithm is presented, which basically involves placing the Longitudes and Latitudes of the polygon for which the area needs to be found into the technique and obtain its area.
Abstract: This paper addresses a simple, easy to implement method for calculating the geographical area of a place on the map using the Shoelace Algorithm. The procedure basically involves placing the Longitudes and Latitudes of the polygon for which the area needs to be found into the technique and obtain its area.
References
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TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Abstract: Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.
8,078 citations
05 Aug 2003
TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).
7,075 citations
01 Jan 2000
TL;DR: In this paper, a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques is proposed for object oriented image processing, which aims for an universal high-quality solution applicable and adaptable to many problems and data types.
Abstract: A necessary prerequisite for object oriented image processing is successful image segmentation. The approach presented in this paper aims for an universal high-quality solution applicable and adaptable to many problems and data types. As each image analysis problem deals with structures of a certain spatial scale, the average image objects size must be free adaptable to the scale of interest. This is achieved by a general segmentation algorithm based on homogeneity definitions in combination with local and global optimization techniques. A scale parameter is used to control the average image object size. Different homogeneity criteria for image objects based on spectral and/or spatial information are developed and compared.
1,672 citations
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TL;DR: A filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria is developed, which outperforms alternatives widely used in gene expression studies.
Abstract: Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach. The proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments. We developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.
266 citations
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01 Jan 2011
TL;DR: In this article, the authors presented a multiscale object-specific analysis (MOSA) approach for multi-scale landscape analysis, which combines Spectral and Textural Information for classifying Remotely Sensed Images.
Abstract: Ch 1 - Basics of Remote Sensing Steven M. de Jong, Freek D. van der Meer & Jan G.P.W. Clevers Ch 2 - Spatial Variability, Mapping Methods, Image Analysis and Pixels Steven M. de Jong, Edzer J. Pebesma & Freek D. van der Meer Ch 3 - Sub-Pixel Methods in Remote Sensing Giles M. Foody Ch 4 - Resolution Manipulation and Sub-Pixel Mapping Peter M. Atkinson Ch 5 - Multiscale Object-Specific Analysis (MOSA): An Integrative Approach for Multiscale Landscape Analysis Geoffrey J. Hay & Danielle J. Marceau Ch 6 - Variogram Derived Image Texture for Classifying Remotely Sensed Images Mario Chica-Olmo & Francisco Abara-Hernandez Ch 7 - Merging Spectral and Textural Information for Classifying Remotely Sensed Images Suha Berberoglu & Paul J. Curran Ch 8 - Contextual Image Analysis Methods for Urban Applications Peng Gong & Bing Xu Ch 9 - Pixel-Based, Stratified and Contextual Analysis of Hyperspectral Imagery Freek D. van der Meer Ch 10 - Variable Multiple Endmember Spectral Mixture Analysis for Geology Applications Klaas Scholte, Javier Garcia-Haro & Thomas Kemper Ch 11 - A Contextual Algorithm for Detection of Mineral Alteration Halos with Hyperspectral Remote Sensing Harald van der Werff & Arko Lucieer Ch 12 - Image Segmentation Methods for Object-Based Analysis and Classification Thomas Blaschke, Charles Burnett & Anssi Pekkarinen Ch 13 - Multiscale Feature Extraction from Images Using Wavelets Luis M.T. deCarvalho, Fausto W. Acerbi Jr., Jan G.P.W. Clevers, Leila M.G. Fonseca & Steven M. de Jong Ch 14 - Contextual Analysis of Remotely Sensed Images for the Operational Classification of Land Cover in the United Kingdom Robin M. Fuller, Geoff M. Smith & Andy G. Thomson Ch 15 - A Contextual Approach to Classify Mediterranean Heterogeneous Vegetation Using the Spatial Reclassification Kernel (SPARK) and DAIS7915 Imagery Raymond Sluiter, Steven M. de Jong, Hans van der Kwast & Jan Walstra
226 citations