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With the rapid developments in the remote sensing technologies and services, there is a necessity for combined compression and encryption of satellite imagery. The onboard satellite compression is used to minimize storage and communication bandwidth requirements of high data rate satellite applications. While encryption is employed to secure these resources and prevent illegal use of image sensitive information. In this paper, we propose an approach to address these challenges which raised in the highly dynamic satellite based networked environment. This approach combined compression algorithms (Huffman and SPIHT) and encryptions algorithms (RC4, blowfish and AES) into three complementary modes: (1) secure lossless compression, (2) secure lossy compression and (3) secure hybrid compression. The extensive experiments on the 126 satellite images dataset showed that our approach outperforms traditional and state of art approaches by saving approximately (53%) of computational resources. In addition, the interesting feature of this approach is these three options that mimic reality by imposing every time a different approach to deal with the problem of limited computing and communication resources.

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Abstract-The use of Compressive Sensing (CS) technique toacquire GPS signals saves processing time when compared with software FFT based receivers. The computational complexity is reduced by up to 60%. Such CS solution uses a fixed size measurement matrix chosen to offer a compromise between the processing overhead and signals acquisition success level. This paper proposes a dynamic acquisition algorithm for GPS signals based on CS. Instead of using a fixed size sampling channels and fixed correlators in the measurement matrix, our DSCR dynamically changes the number and size of the required Channels/Correlators according to the received GPS signals power during acquisition. This adaptive solution offers better fix capability when the GPS receiver is located in harsh signal environment or it will save valuable processing/decoding time (battery power, especially for Smartphones) when the receiver outdoors. A feedback loop is devised to control the sampling channels number and resize the measurement matrix.

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