An Overview of Technological Revolution in Satellite Image Analysis <s> Statistical Methods <s> Abstract In this paper, an iterative statistical approach is proposed to fuse spectral information with spatial and temporal contextual information for the classification of multitemporal, multisensor remote-sensing images. Experimental results show, in terms of classification accuracy, the improvement that can be reached by exploiting the contextual information. <s> BIB001 </s> An Overview of Technological Revolution in Satellite Image Analysis <s> Statistical Methods <s> Frost is a perennial agricultural hazard that normally causes crop damage leading to huge agricultural losses within the Kenyan highlands; aggravated by inadequate information on frost. This research mapped frost hotspots within the Aberdare and Mount Kenya regions and identified the extent of arable land under frost risk while establishing the trend of minimum temperature occurrences between the years 2000 and 2013. Minimum temperature values were extracted from daily Moderate Resolution Imaging Spectroradiometer land surface temperature data-sets, and frost risk categorized into very severe frost (<250 K), severe frost (250–260 K), moderate frost (260–270 K), minor frost (270–280 K) and areas of no frost. Concentration of frost (<273 K) was mapped within regions above 1500 m asl and occasional occurrences within valleys lower than this altitude with recurrent occurrences in the months of April, May, July, August and November. Elevation, land surface convexity and humidity were found to influence frost o... <s> BIB002
The conventional spatial models are based on statistical methods, which are highly dependent on spectral only information. It can achieve better accuracy but the lack of reasoning leads to reduced prediction rate than machine learning models. D.I.F  merged the estimates of ground and satellite derived rainfall observations of same region through calculating a weighted average of both the satellite and guage values for each satellite pixel  . Farid BIB001 made an attempt to fuse spectral information with spatial and temporal information using Markov Random Fields (MRF). The results showed improved classification accuracy with the increased level of contextual information BIB001 . Susan Malaso, (2015) has developed a Frost risk mapping model using pixel based classification of satellite images in manipulating the minimum temperature BIB002 . It is a highly evident mode of satellite image interpretation where the extracted temperature values are analysed for tracing the previous change outcome or for future prediction. In order to estimate the temperature from brightness values of the satellite image pixels, the proper methods for extraction of spectral reflectance is must. The Landsat 7 Science Data User's Handbook procedure can be followed to estimate the temperature from Landsat data. The following Tab. 1 illustrates few works from the literature that applies statistical methods for satellite image interpretation. The previous studies and works on statistical image classification methods clearly reveal that improving the existing statistical based image classification algorithms alone is insufficient to achieve high level of accuracy. Hence, it is profound to step up for new schemes in image classification using the recent advances in ML. 