In order to effectively store and transmit MODIS multispectral data, a lossless compression method based on mix coding
and integer wavelet transform (IWT) is proposed in this paper. Firstly, the algorithm computes the correlation
coefficients between spectrums in MODIS data. Using proper coefficient threshold, the original bands will be divided
two groups: one group use spectral prediction method and then compress residual error, while the other group data is
directly compressed by some standard compressor. For the spectral prediction group, we can find the current band that
has greatest correlation with the previous band by the judgments of correlation coefficient, thus the optimal spectral
prediction sequence is obtained by band reordering. The prediction band data can be computed with the previous band
data and optimal linear predictor, so the spectral redundancy can be eliminated by using spectral prediction. In order to
reduce residual differences in further, the block optimal linear predictor is designed in this paper. Next, except for the
first band of the spectral prediction sequence, the residual errors of other bands are encoded by IWT and SPIHT. The
direct compression bands and the first band of spectral prediction sequence are compressed by JPEG2000. Finally, the
coefficients of block optimal linear predictor and other side information are encoded by adaptive arithmetic coding. The
experimental results show that the proposed method is efficient and practical for MODIS data.
Cloud is one of common noises in MODIS remote sensing image. Because of cloud interference, much important
information covered with cloud can't be obtained. In this paper, an effective method is proposed to detect and remove
thin clouds with single MODIS image. The proposed method involves two processing-thin cloud detection and thin
cloud removal. As for thin cloud detection, through analyzing the cloud spectral characters in MODIS thirty-six bands,
we can draw the conclusion that the spectral reflections of ground and cloud are different in various MODIS band.
Hence, the cloud and ground area can be separately identified based on MODIS multispectral analysis. Then, the region
label algorithm is used to label thin clouds from many candidate objects. After cloud detection processing, thin cloud
removal method is used to process each cloud region. Comparing with traditional methods, the proposed method can
realize thin cloud detection and removal with single remote sensing image. Additionally, the cloud removal processing
mainly aims to the cloud label region rather than the whole image, so it can improve the processing efficiency.
Experiment results show the method can effectively remove thin cloud from MODIS image.
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