Satellite Image Compression Using Compressive Sensing
Satellite images are multispectral or hyperspectral images, which are important for earth observation and military
reconnaissance. However, there exists big incompatibility between the transmission capacity of satellite channel and large amount
hyper spectral data. There are spatial and spectrum redundancy in hyper spectral image. As to exploit spectrum correlation
sufficiently, it must be to pre-process hyper spectral image. In this paper, we propose a novel acceptable complexity lossy hyper
spectral image compression scheme. Conventional approaches to sampling signals or images follow Shannon’s celebrated theorem,
which says that the sampling rate must be at least twice the signal bandwidth (the so-called Nyquist rate). In fact, this principle
underlies nearly all signal acquisition protocols used in consumer audio and visual electronics, medical imaging devices, radio
receivers, and so on. Compressive sampling, also known as compressed sensing or CS, a novel sensing sampling paradigm that goes
against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer
samples or measurements than traditional methods used.
Keywords: Image compression; compressive sensing (CS); sparsity; discrete cosine transform (DCT); isometry; orthogonal pursuit
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