Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection
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.
A novel theory of information acquisition-"compressive sampling" has been applied in this paper, and goes against the
common wisdom in data acquisition of Shannon theorem. CS theory asserts that one can recover certain signals and
images perfectly from far fewer samples or measurements than traditional methods use. This paper presents an
improvement on genetic algorithm instead of match pursuit algorithm in consideration of the enormous computational
complexity on sparse decomposition. Then the whole image is divided into small blocks which can be processed by
sparse decomposition, and an end to decomposition is determined by PSNR threshold adaptively. At last, the experiment
results show that good performance on image reconstruction with less computational complexity has been achieved.
A new lossless compression method based on prediction tree with error compensation for hyperspectral imagery is
proposed in this paper. This method incorporates the techniques of prediction tree and adaptive band prediction. The
proposed method is different from previous similar approaches in that its prediction to the current band is performed by
multiple bands and the error created by the prediction tree is compensated by a linear adaptive predictor for decorrelating
spectral statistical redundancy. After de-correlating intraband and interband redundancy, the SPIHT (Set
Partitioning in Hierarchical Trees) wavelet coding is used to encode the residual image. The proposed method achieves
high compression ratio on the NASA JPL AVIRIS data.