This PDF file contains the front matter associated with SPIE Proceedings Volume 8871, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed “Z-Chrome” algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .

Typical distributed video coding architecture always controls rate at the decoder via a feedback channel, which is not realistic in application. In order to remove the feedback channel, an efficient encoder rate control method is proposed for unidirectional distributed video coding in this paper. First a low-complexity motion estimation method is proposed to create estimated side information at the encoder, in which the motion consistency between frames is utilized to reduce the search range and obtain accurate motion vectors. Then the conditional entropy of each bitplane is computed based on the inter-bitplane correlation, which is further curve-fitted as the estimated rate. Experimental results show that our proposed encoder rate control method could estimate the required rate accurately with much lower complexity.

Optical code-division multiple access (OCDMA) systems usually allocate orthogonal or quasi-orthogonal codes to the active users. When transmitting through atmospheric scattering channel, the coding pulses are broadened and the orthogonality of the codes is worsened. In truly asynchronous case, namely both the chips and the bits are asynchronous among each active user, the pulse broadening affects the system performance a lot. In this paper, we evaluate the performance of a 2D asynchronous hard-limiting wireless OCDMA system through atmospheric scattering channel. The probability density function of multiple access interference in truly asynchronous case is given. The bit error rate decreases as the ratio of the chip period to the root mean square delay spread increases and the channel limits the bit rate to different levels when the chip period varies.

By fully exploiting the high correlation of the pixels along an edge, a new lossless compression algorithm for hyperspectral images using adaptive edge-based prediction is presented in order to improve compression performance. The proposed algorithm contains three modes in prediction: intraband prediction, interband prediction, and no prediction. An improved median predictor (IMP) with diagonal edge detection is adopted in the intraband mode. And in the interband mode, an adaptive edge-based predictor (AEP) is utilized to exploit the spectral redundancy. The AEP, which is driven by the strong interband structural similarity, applies an edge detection first to the reference band, and performs a local edge analysis to adaptively determine the optimal prediction context of the pixel to be predicted in the current band, and then calculates the prediction coefficients by least-squares optimization. After intra/inter prediction, all predicted residuals are finally entropy coded. For a band with no prediction mode, all the pixels are directly entropy coded. Experimental results show that the proposed algorithm improves the lossless compression ratio for both standard AVIRIS 1997 hyperspectral images and the newer CCSDS test images.

Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression.

In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

Most artificial neural networks use a nonlinear activation function that includes sigmoid and hyperbolic tangent functions. Most artificial networks employ nonlinear functions such as these sigmoid and hyperbolic tangent functions, which incur high complexity costs, particularly during hardware implementation. In this paper, we propose new polynomial approximation methods for nonlinear activation functions that can substantially reduce complexity without sacrificing performance. The proposed approximation methods were applied to pattern classification problems. Experimental results show that the processing time was reduced by up to 50% without any performance degradations in terms of computer simulation.

In this study, we propose an efficient classification which combines signal subspace projection (SSP) and partial filtering technique for hyperspectral images. To reduce the computation complexity in image classification, we exploit high degree correlations in spectral and spatial domains. During training process, image bands are first partitioned into several groups for each desired class by Maximum Correlation Band Clustering (MCBC) approach. Then, we design partial filters for each band group by SSP approach. Finally, the SSP-based partial filtering (SSPPF) are combined using corresponding weights for each class. For real image classification, simulations validate the proposed SSPPF can achieve the performance of SSP with less computation complexity. Generally, the proposed method requires only 1/ k² computations of SSP, if image is partitioned into k groups.

Remote sensing technology can extract useful information from observation areas, meanwhile provide effective data for land monitoring, which is widely used in dynamic monitoring and resources research of saline alkali land. Through using MODIS spectral remote sensing data, a case study of Western Jilin Province of China mainly covered by typical saline alkali land was carried out in this paper. After using the proposed optimal band combination method, the main distribution positions of the observed saline alkali land were roughly determined based on the colors and shapes of MODIS images derived from deferent seasons. After analyzing the time series of NDVI observations, the decision tree classification of land cover was designed to determine the land cover types and the degree of salinity-alkalinity. Through obtaining and analyzing of the spectral characteristics of each saline alkali land type, the relationship between the spectral characteristics and saline alkali land type was deduced. The research results demonstrated that the saline alkali lands located in Western Jilin Province, China were effectively classified based on the spectral characteristics of MODIS data, which provided the moderate spatial resolution classification results for a wide range of saline alkali land monitoring.

A non-secure transmission channel is considered as a major challenge in remote sensing. The commercial value of satellite imagery and the sensitive information it contains led engineers to look for different means to secure the ownership of satellite imagery and preventing the illegal use of these resources. Therefore, a blind multi-watermarking scheme for copyright protection and image authentication is proposed. The multi-watermarking scheme is based on designing two back-to-back encoders. The first encoder embeds a robust ownership watermark in a frequency domain of satellite imagery using Discrete Cosine Transform (DCT) approach. Whereas, the second encoder embeds a fragile authentication information into a spatial domain of a watermarked image using Message Digest Encryption Key algorithm. This study was conducted on DubaiSat-1 satellite imagery owned by Emirates Institution for Advanced Science and Technology (EIAST). The simulation results demonstrate that the proposed scheme is robust against many intentional and unintentional attacks. Moreover, it shows a very high ability for tamper detection.

Virtual dimensionality (VD) has received considerable interest where VD is used to estimate the number of spectral distinct signatures, denoted by p. Unfortunately, no specific definition is provided by VD for what a spectrally distinct signature is. As a result, various types of spectral distinct signatures determine different values of VD. There is no one value-fit-all for VD. In order to address this issue this paper presents a new concept, referred to as anomaly-specified VD (AS-VD) which determines the number of anomalies of interest present in the data. Specifically, two types of anomaly detection algorithms are of particular interest, sample covariance matrix K-based anomaly detector developed by Reed and Yu, referred to as K-RXD and sample correlation matrix R-based RXD, referred to as R-RXD. Since K-RXD is only determined by 2^nd order statistics compared to R-RXD which is specified by statistics of the first two orders including sample mean as the first order statistics, the values determined by K-RXD and R-RXD will be different. Experiments are conducted in comparison with widely used eigen-based approaches.

Spectral unmixing is an important research hotspot for remote sensing hyperspectral image applications. The unmixing process is comprised of the extraction of spectrally pure signatures (also called endmembers) and the determination of the abundance fractions of endmembers. Due to the inconspicuous signatures of pure spectra and the challenge of inadequate spatial resolution, sparse regression (SR) techniques are adopted in solving the linear spectral unmixing problem. However, the spatial information has not been fully utilized by state-of-art SR-based solutions. In this paper, we propose a new unmixing algorithm which involves in more suitable spatial correlations on sparse unmixing formulation for hyperspectral image. Our algorithm integrates the spectral and spatial information using Adapting Markov Random Fields (AMRF) which is introduced to exploit the spatial-contextual information. Compared with other SR-based linear unmixing methods, the experimental results show that the method proposed in this paper not only improves the characterization of mixed pixels but also obtains better accuracy in hyperspectral image unmixing.

A better understanding of the relationship between satellites observed vegetation health, and malaria epidemics could help mitigate the worldwide increase in incidence of mosquito-transmitted diseases. This research investigates last 17- years association between vegetation health (condition) index and malaria transmission in Bikaner, Rajasthan in India an arid and hot summer area. The vegetation health (condition) index, derived from a combination of Advanced Very High Resolution Radiometer (AVHRR) based Normalized Difference Vegetation Index (NDVI) and 10-μm to 11-μm thermal radiances, was designed for monitoring moisture and thermal impacts on vegetation health. We demonstrate that thermal condition is more sensitive to malaria transmission with different seasonal malaria activities. The weekly VH indices were correlated with the epidemiological data. A good correlation was found between malaria cases and Temperature Condition Index (TCI) one at least two months earlier than the malaria transmission season. Following the results of correlation analysis, Principal Component Regression (PCR) method was used to construct a model of less than 10% error to predict malaria as a function of the TCI.

This paper presents an advanced method for the automatic recognition of bridges over water in high resolution satellite image data, intended for an application on-board of satellites. The algorithm is implemented in recon gurable hardware, a so-called Field Programmable Gate Array (FPGA). Within a few seconds a thematic map is derived from the original satellite image. The map contains information about the water areas, islands and bridge deck areas in the captured scene. No a-priory knowledge is needed. Due to the autonomous image processing and the low power consumption of the FPGA, this implementation seems suitable for an application on-board of satellites. Especially in case of a natural disaster it could provide quick information about accessible transportation routes. The algorithm as well as experimental results on panchromatic and near-infrared satellite imagery are presented in this article. The obtained results are promising.

The Pixel Purity Index (PPI) algorithm is one of the most successful algorithms for hyperspectral image endmembers extraction. But it has high computational complexity so it is hard to meet the real-time processing demands of some onboard application. In this paper, we present a novel Very-Large-Scale Integration (VLSI) architecture for PPI algorithm to meet the on-board demands. With parallelism and improved I/O communication strategy, our implementation is significantly time saving than other architectures in the same hardware resources. We evaluate our implementation using the well-known “Cuprite” scene and assess endmembers signature purity using the U.S. Geological Survey (USGS) library. It demonstrates that our hardware implementation can get endmembers in less processing time to meet the onboard demands.

Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophic zones. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. Microcystis has a unique advantage over its competition as a result of the invasive zebra mussel population that filters algae out of the water column except for the toxic Microcystis. The toxin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This presentation demonstrates the prototype of a near real-time early warning system using Integrated Data Fusion techniques with the aid of both hyperspectral remote sensing data to determine spatiotemporal microcystin concentrations. The temporal resolution of MODIS is fused with the higher spatial and spectral resolution of MERIS to create synthetic images on a daily basis. As a demonstration, the spatiotemporal distributions of microcystin within western Lake Erie are reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the this type of algorithm has better potential to accurately estimating microcystin concentrations in the lake, which is better than current two band models and other computational intelligence models.

DubaiSat-1 (DS1) captures multispectral images, which consist of three visible channels and one NIR with 5-meter resolution and a panchromatic channel with 2.5-meter resolution. Considering these resolutions, some important details might appear blurry on the image. Therefore, the concept of “Super Resolution” has been introduced to reconstruct the image in a way to overcome the inherent resolution limitations of imaging sensors. The aim of this study is to enhance the quality of the image by artificially increasing the number of pixels within the image to sharpen out-of-focus details or smooth rough edges in DubaiSat-1 images that have been enlarged using a general up-scaling process. Usually, images received from satellites go through many image processing and enhancement steps to increase the quality of these images. Many studies have been conducted in this field, and different techniques were suggested to improve the enhancement procedure. Studies were done to combine low resolution images of the same area to come up with an image with a high resolution and better quality. Image enhancement refers to operations used to get image features such edges, boundaries, or contrast in order to make a digital image more useful for display and analysis. In this paper, the proposed method is based on a well-known approach called “Example Based Super-Resolution”. A single low resolution DubaiSat-1 image is used to construct its high resolution version. Simulation results illustrate a good performance of super-resolved images over certain magnification factors (i.e. M = 2,3 and 4).

Satellite imagery always has low-resolution causing poor application in practice because the serious degradation in imaging is resulted in many factors such as atmospheric turbulence, cloud, and aberration of optical system. To reconstruct the degraded remote sensing images with a high quality, we designed an algorithm to estimate the system modulation transfer function (MTF) accurately. Phase congruency is employed to detect the edges and corners of the image first, then the significant edges, which are utilized to estimate the edge spread function (ESF) using inclined edge method, are picked up from above features through a certain line detection measurement. An image restoration algorithm based on total variation (TV) is introduced to deconvolute the degraded image with the estimated MTF which is derived from the ESF. The experiments show that this method is adaptive and efficient to recover the remote sensing images taken from a Chinese Satellite. The restored images with a higher resolution and higher signal-to-noise ratio (SNR) will improve the applications greatly.

Band selection (BS) has advantages over data dimensionality in satellite communication and data transmission in the sense that spectral bands can be selected by users at their discretion for data analysis, while preserving data fidelity. However, to materialize BS in such practical applications several issues need to be addressed. One is how many bands required for BS. Another is how to select appropriate bands. A third one is how to take advantage of previously selected bands without re-implementing BS. Finally and most important one is how to process BS as number of bands varies. This paper presents a specific application to progressive band processing of anomaly detection, which does not require BS and can be carried out in a progressive fashion with data updated recursively band by band in the same way that data is processed by a Kalman filter.

The blended sea surface temperature (SST), which generated from satellites-retrieved SST, is widely used in the fields of oceanic and atmospheric researches. Due to the quality of satellites-retrieved SST will affect the blended SST, the quality control (QC) is necessary and important. In general, the quality of data is controlled by the in situ observations. However, the in situ SST observations are sparse and not available in near real time over the globally ocean, especially for the China Seas and their adjacent seas. In this paper, a complementary quality control procedure, which use the Optimal Interpolation SST as a reference standard (TR) to identify outliers in infrared SST (TS) is proposed. The TR is validated against in situ SST first. Then a time evolution check for TS is employed. The TS lies between the limit checks, which are defined relative to TR of the previous 10 days. Spatial-coherence analyses of the differences (ΔSST) between the TS and the TR is taken into account later on. Then, robust statistics is applied to flag the extreme residual outliers. After those QC procedures for MODIS-retrieved SST, most of the outliers are removed. The histogram of ΔSST is strong asymmetry and the minimum value reach ~-35°C mainly due to the cloud contamination before QC. The corresponding histogram after QC shows that the ΔSST are close to Gaussian and the min and max ΔSST reach~ ±4°C. The further validation for this method is performed using a total number of 506 matchups of buoy and MODIS. The bias is -0.458 and the standard deviation is 1.341. This QC procedure can effectively remove the outliers and the remaining observation errors are mainly due to diurnal variability, which should be focused on in the future study.

In the study, we propose an efficient subspace-based semiblind channel estimation for multiple-input–multiple-output (MIMO) space–time code (STC) orthogonal frequency-division multiplexing (OFDM) systems. We first proposed a forward-backward estimation (FBE) method which can improve the channel estimation accuracy by using both the forward and backward receiving data. Then, based on the symmetric property of the forward and backward smoothed correlation matrix, we develop a fast forward-backward (FFB) estimation method which estimates the noise subspace by performing eigen-decomposition of two half dimensionality sub-matrices obtained from the forward and backward smoothed correlation matrix. FFB achieves the same performance as the FBE but only requires one-fourth computation complexity of FBE. Computer simulations demonstrate the effectiveness and accuracy in channel estimation of the proposed FFB for the MIMO STC-OFDM systems.

The widely used principal component analysis (PCA) is implemented in nonlinear by an auto-associative neural network. Compared to other nonlinear versions, such as kernel PCA, such a nonlinear PCA has explicit encoding and decoding processes, and the data can be transformed back to the original space. Its data compression performance is similar to that of PCA, but data analysis performance such as target detection is much better. To expedite its training process, graphics computing unit (GPU)-based parallel computing is applied.

It is very important to extract the useful information from weak laser signal which is obtained in complex battlefield environment as laser warning taking an increasingly important role in laser countermeasures. The weak signal merging in noise becomes difficult to detect since the signal to noise ratio (SNR) of the signal received by the laser warning system is very low in real battlefield. Traditional signal detection methods, in which only mean filter or wiener filter are used; perform poorly in improving the SNR of the signals. A modified matrix of Hadamard Transform based on the Weighting Theory, overcame the disadvantages of matrices that are commonly used to cope with the low SNR signal. The modified matrix generating method of Hadamard Transform is introduced in detail, and then theory analysis, calculations and simulations on the modified matrix Hadamard Transform are presented. The results showed that this kind of Hadamard Transform performs excellently in increasing detection probability and decreasing False Alarm Ratio (FAR) of the laser warning system.

In the paper, combined optimization of the terminal precoders/equalizers and single-relay precoder is proposed for an amplify-and-forward (AF) multiple-input multiple-output (MIMO) two-way single-relay system with correlated channel uncertainties. Both terminal transceivers and relay precoding matrix are designed based on the minimum mean square error (MMSE) criterion when terminals are unable to erase completely self-interference due to imperfect correlated channel state information (CSI). This robust joint optimization problem of beamforming and precoding matrices under power constraints belongs to neither concave nor convex so that a nonlinear matrix-form conjugate gradient (MCG) algorithm is applied to explore local optimal solutions. Simulation results show that the robust transceiver design is able to overcome effectively the loss of bit-error-rate (BER) due to inclusion of correlated channel uncertainties and residual self-interference.