Many compression methods have been developed until now, especially for very high-resolution satellites images, which, due to the massive information contained in them, need compression for a more efficient storage and transmission. This paper modifies Perfilieva's Fuzzy transform using pseudo-exponential function to compress very high-resolution satellite images. We found that very high-resolution satellite images can be compressed by F-transform with pseudo-exponential function as the membership function. The compressed images have good quality as shown by the PSNR values ranging around 59-66 dB. However, the process is quite time-consuming with average 187.1954 seconds needed to compress one image. These compressed images qualities are better than the standard compression methods such as CCSDS and Wavelet method, but still inferior regarding time consumption.

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Geometric correction is necessary in photogrammetry and remote sensing to avoid geometric distortions and establish relationship between image coordinate and its corresponding ground coordinate. Rigorous sensor model is one of the methods which considered the most precise and accurate for geometric correction. However, as rigorous sensor model contains many equations that depend on the actual physical properties of the sensor, the model is specialized for each sensor. In this paper, we modified a rigorous sensor model of geometric correction for pushbroom imager into geometric correction model for dual sensor pushbroom imager. The result shows that a model has been successfully obtained and can be used to geometrically correct coordinates of dual sensor pushbroom imagery.

The current urban environment is very dynamic and always changes both physically and socio-economically very quickly. Monitoring urban areas is one of the most relevant issues related to evaluating human impacts on environmental change. Nowadays remote sensing technology is increasingly being used in a variety of applications including mapping and modeling of urban areas. The purpose of this paper is to classify the Pleiades data for the identification of roof materials. This classification is based on data from satellite image spectroscopy results with very high resolution. Spectroscopy is a technique for obtaining spectrum or wavelengths at each position from various spatial data so that images can be recognized based on their respective spectral wavelengths. The outcome of this study is that highresolution remote sensing data can be used to identify roof material and can map further in the context of monitoring urban areas. The overall value of accuracy and Kappa Coefficient on the method that we use is equal to 92.92% and 0.9069.

The Community Satellite Processing Package (CSPP) is developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the Space Science and Engineering Center (SSEC) at the University of Wisconsin to support Direct Broadcast (DB) communities in processing various remote sensing satellite data. Currently, there are eleven satellites and 25 instruments supported by fifteen CSPP packages. Some packages have dependency with another package when an output of a package become an input to another package. However, there is no document that describes relationships among input and output data of the packages. Each package is only accompanied by its installation instruction and user manual. Thus, a main document describing interdependency of the packages is required. This paper can act as the main document because it describes the current development of the CSPP packages and their interdependency among one another. Information was gathered through installation, user manual and literature (proceedings and journal articles) reviews. The results are illustrated in term of intuitive diagrams that are not available in the current instructions or manuals. Thus, the main document that is described in this paper can be utilized to identify gaps that should be taken into considerations when developing the existing system in the future.