An Overview of Technological Revolution in Satellite Image Analysis <s> Remote Sensing Experts <s> The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. We have developed a methodology to map and monitor land cover change using multitemporal Landsat Thematic Mapper (TM) data in the seven-county Twin Cities Metropolitan Area of Minnesota for 1986, 1991, 1998, and 2002. The overall seven-class classification accuracies averaged 94% for the four years. The overall accuracy of land cover change maps, generated from post-classification change detection methods and evaluated using several approaches, ranged from 80% to 90%. The maps showed that between 1986 and 2002 the amount of urban or developed land increased from 23.7% to 32.8% of the total area, while rural cover types of agriculture, forest and wetland decreased from 69.6% to 60.5%. The results quantify the land cover change patterns in the metropolitan area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyze changes in land cover over time that can be used as inputs to land management and policy decisions. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Remote Sensing Experts <s> Abstract This paper deals with the limitations of visual interpretation of high-resolution remote sensing images and of automatic computer classification completely dependent on spectral data. A knowledge-rule method is proposed, based on spectral features, texture features obtained from the gray-level co-occurrence matrix, and shape features. QuickBird remote sensing data were used for an experimental study of land-use classification in the combination zone between urban and suburban areas in Beijing. The results show that the deficiencies of methods where only spectral data are used for classification can be eliminated, the problem of similar spectra in multispectral images can be effectively solved for the classification of ground objects, and relatively high classification accuracy can be reached. <s> BIB002 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Remote Sensing Experts <s> Geological images, such as photos and photomicrographs of rocks, are commonly used as supportive evidence to indicate geological processes. A limiting factor to quantifying images is the digitization process; therefore, image analysis has remained largely qualitative. ArcGIS^(R), the most widely used Geographic Information System (GIS) available, is capable of an array of functions including building models capable of digitizing images. We expanded upon a previously designed model built using Arc ModelBuilder^(R) to quantify photomicrographs and scanned images of thin sections. In order to enhance grain boundary detection, but limit computer processing and hard drive space, we utilized a preprocessing image analysis technique such that only a single image is used in the digitizing model. Preprocessing allows the model to accurately digitize grain boundaries with fewer images and requires less user intervention by using batch processing in image analysis software and ArcCatalog^^^(R). We present case studies for five basic textural analyses using a semi-automated digitized image and quantified in ArcMap^(R). Grain Size Distributions, Shape Preferred Orientations, Weak phase connections (networking), and Nearest Neighbor statistics are presented in a simplified fashion for further analyses directly obtainable from the automated digitizing method. Finally, we discuss the ramifications for incorporating this method into geological image analyses. <s> BIB003 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Remote Sensing Experts <s> AbstractAccurate and reliable drought monitoring is essential to drought mitigation efforts and reduction of social vulnerability. A variety of indices, such as the standardized precipitation index (SPI), are used for drought monitoring based on different indicator variables. Because of the complexity of drought phenomena in their causation and impact, drought monitoring based on a single variable may be insufficient for detecting drought conditions in a prompt and reliable manner. This study outlines a multivariate, multi-index drought monitoring framework, namely, the multivariate standardized drought index (MSDI), for describing droughts based on the states of precipitation and soil moisture. In this study, the MSDI is evaluated against U.S. Drought Monitor (USDM) data as well as the commonly used standardized indices for drought monitoring, including detecting drought onset, persistence, and spatial extent across the continental United States. The results indicate that MSDI includes attractive propert... <s> BIB004
The Remote sensing community includes geologists, earth scientists, climate scientists and archaeologists etc-who relies more on the field study than ML community. They use the ground measurements from sensors installed in the field of study for validation of models. The use of Geographical Information System (GIS) tools which is highly compatible for exploiting the necessary information from satellite images with built-in functions for complex level of processing is also more prevalent among this community. J.S. Rawat et al. (2013) performed a supervised classification for detecting land cover change in the chosen study area using maximum likelihood approach in Erdas Imagine  . Fei BIB001 performed initial land cover classification using guided clustering and maximum likelihood approach. And post classification to resolve the errors is carried out using a rule-based spatial model in Erdas Imagine BIB001 . Michael A. DeVasto et al. (2012) performed automated grain boundary detection in satellite images using ArcGIS BIB003 . Tapas R. Martha et al., in 2014 performed visual analysis of change estimation in pan sharpened very high resolution images for detecting damage estimation using Erdas Imagine  . The use of derived indices along with geospatial tools analysis is also more popular among this research community in tracing or predicting the environmental changes. The combination of different vegetation indices could achieve a better prediction. Usually, the meteorological drought occurs a few months earlier than agricultural drought and use of single index is a serious limitation in drought monitoring. Hence integration of multiple indices is a good choice BIB004 . Zhang Rongqun (2011) proposed a knowledge rule based approach with integrated spectral and spatial features to deal with the limits on spectral information based classification since the spectral information is insufficient to discriminate the objects with similar spectra. BIB002 .