Texture Measures ofSpatial Patterns on Thematic Mapper Imagery: An Experiment.

The digital format of remote sensing data facilitates the measurement of spatial patterns. The concept of measurability of spatial patterns has important geographic implications and may open up an alternative applications for satellite remote sensing of urban areas. Texture analysis, a set of techniques developed in pattern recognition, is found to be useful in measuring spatial pattern on digital imagery. Two approaches of texture analysis are selected. One is Haralick\u27s Spatial Dependence M atrix, the other is Jernigan\u27s, et. al., Entropy-based texture measures. They perform in spatial domain and frequency domain respectively. Ten subimage areas in Omaha suburb are selected from a Landsat TM image. The subimage areas includes the major residential spatial patterns in the area, Through analysis, it is found that residential areas with d iffe re n t spatial features do present distinguishable texture measures, in both SPADEP and Entropy-based texture analysis. With, the introduction of texture analysis, a new set of terminology can be used to describe a spatial pattern and may greatly enhance our concepts of certain spatial phenomena. Potential application of texture analysis in this context could be in urban land use mapping, computer-assisted land use monitoring and comparative study in urban spatial patterns

Analysis of GLCM Parameters for Textures Classification on UMD Database Images

Texture analysis is one of the most important techniques that have been used in image processing for many purposes, including image classification. The texture determines the region of a given gray level image, and reflects its relevant information. Several methods of analysis have been invented and developed to deal with texture in recent years, and each one has its own method of extracting features from the texture. These methods can be divided into two main approaches: statistical methods and processing methods. Gray Level Co-occurrence Matrix (GLCM) is the most popular statistical method used to get features from the texture. In addition to GLCM, a number of equations of Haralick characteristics will be used to calculate values used as discriminate features among different images in this study. There are many parameters of GLCM that should be taken into consideration to increase the discrimination between images belonging to different classes. In this study, we aim to evaluate GLCM parameters. For three decades now, GLCM is popular method used for texture analysis. Neural network which is one of supervised methods will also be used as a classifier. And finally, the database for this study will be images prepared from UMD (University of Maryland database)

Data compression techniques applied to high resolution high frame rate video technology

An investigation is presented of video data compression applied to microgravity space experiments using High Resolution High Frame Rate Video Technology (HHVT). An extensive survey of methods of video data compression, described in the open literature, was conducted. The survey examines compression methods employing digital computing. The results of the survey are presented. They include a description of each method and assessment of image degradation and video data parameters. An assessment is made of present and near term future technology for implementation of video data compression in high speed imaging system. Results of the assessment are discussed and summarized. The results of a study of a baseline HHVT video system, and approaches for implementation of video data compression, are presented. Case studies of three microgravity experiments are presented and specific compression techniques and implementations are recommended