Shuxu Guo

Automatic Thick Cloud Removal for MODIS Remote Sensing Imagery

In this paper, an automatic method is proposed to realize thick cloud removal for Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. The proposed method can make full use of MODIS advantages of high temporal resolution and spatial resolution. The overlapping region can be detected by utilizing geographical information of the thick cloud data and no-cloud data, then SIFT detection and feature point matching are applied in the overlapping region, furthermore, the exact matching point pairs can be extracted with proper strategy. Based on these exact matching point pairs and the quadratic polynomial model, the rectified image can be obtained. Meanwhile, thick cloud regions are detected by the algorithm of multispectral image analysis, and then the images of thick cloud regions are replaced with the corresponding regions of the rectified image. Finally, radiance differences are eliminated for image visual effect. Experiment results demonstrate that the proposed method can effectively remove thick cloud from MODIS image, which can satisfy the demand of post-processing for remote sensing imagery.

An Improved High-Sensitivity Airborne Transient Electromagnetic Sensor for Deep Penetration

The investigation depth of transient electromagnetic sensors can be effectively increased by reducing the system noise, which is mainly composed of sensor internal noise, electromagnetic interference (EMI), and environmental noise, etc. A high-sensitivity airborne transient electromagnetic (AEM) sensor with low sensor internal noise and good shielding effectiveness is of great importance for deep penetration. In this article, the design and optimization of such an AEM sensor is described in detail. To reduce sensor internal noise, a noise model with both a damping resistor and a preamplifier is established and analyzed. The results indicate that a sensor with a large diameter, low resonant frequency, and low sampling rate will have lower sensor internal noise. To improve the electromagnetic compatibility of the sensor, an electromagnetic shielding model for a central-tapped coil is established and discussed in detail. Previous studies have shown that unclosed shields with multiple layers and center grounding can effectively suppress EMI and eddy currents. According to these studies, an improved differential AEM sensor is constructed with a diameter, resultant effective area, resonant frequency, and normalized equivalent input noise of 1.1 m, 114 m2, 35.6 kHz, and 13.3 nV/m2, respectively. The accuracy of the noise model and the shielding effectiveness of the sensor have been verified experimentally. The results show a good agreement between calculated and measured results for the sensor internal noise. Additionally, over 20 dB shielding effectiveness is achieved in a complex electromagnetic environment. All of these results show a great improvement in sensor internal noise and shielding effectiveness.

A lossless compression method based on mix coding and IWT for MODIS image

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.

Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used — optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1–3 voxels is achieved.

The separation of 1/f noise of semi-conductor laser based on GPU parallel computing

This paper presents a parallel algorithm designed for 1/f noise signal estimation based on Compressed sensing theory on the GPU platform. In the accelerating process, we select parts of the serial program as the object to be speeded up for the execution time of algorithm. Compared with the conventional methods for 1/f noise estimation, our scheme has shown a 20x speedup.

The method of evaluation for 1/f low-frequency electronics noise based on compressed sensing

In this paper, we aimed to separate the 1/f noise from the original signal, and analyzed its characteristics of power spectrum. First, an N-level wavelet transform has been applied to the original data signal before the compressed sensing observation for the original signal. Compared with the tradition measurement procession of compressed sensing, the measurement matrix here is replaced with the circulant matrix. This can greatly reduce the measurement number compared with the random Gaussian matrix. To reduce the algorithm time, some zero independent elements are introduced to the circulant matrix. This proposed circulant matrix is then helpful to save 60 percent of algorithm’s reconstruction time.

Hyperspectral image lossless compression algorithm based on error compensated prediction tree of multi-band prediction

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.

Iterative detection for differential decode-and-forward cooperative communication

In this paper, We proposed an iterative method to estimate the instantaneous probability of error (IPE) and carry maximum likelihood (ML) detection for differential decode-and-forward (DDF) transmission scheme of two-user cooperative communication system. With this method, the transmission of source-to-relay SNR from relay to destination, which is necessary in the previous scheme [4], is not required. The bit error rate (BER) performance is better than previous scheme [4] which is based on average probability of error (APE)[3].