• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.984-996
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• 2014

In this paper, optimal sensing time allocation for adaptive multiband spectrum sensing-transmission procedure is investigated. The sensing procedure consists of an exploration phase and a detection phase. We first formulate an optimization problem to maximize the throughput by designing not only the overall sensing time, but also the sensing time for every stage in the exploration and detection phases, while keeping the miss detection probability for each channel under a pre-defined threshold. Then, we transform the initial non-convex optimization problem into a convex bilevel optimization problem to make it mathematically tractable. Simulation results show that the optimized sensing time setting in this paper can provide a significant performance gain over the previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1131-1145
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• 2013

Energy detection is a widely used method for spectrum sensing in cognitive radios due to its simplicity and accuracy. However, it is severely affected by the noise uncertainty. To solve this problem, a blind multiband spectrum sensing scheme which is robust to noise uncertainty is proposed in this paper. The proposed scheme performs spectrum sensing over the total frequency channels simultaneously rather than a single channel each time. To improve the detection performance, the proposal jointly utilizes the likelihood function combined with Gerschgorin radii of unitary transformed covariance matrix. Unlike the conventional sensing methods, our scheme does not need any prior knowledge of noise power or PU signals, and thus is suitable for blind spectrum sensing. In addition, no subjective decision threshold setting is required in our scheme, making it robust to noise uncertainty. Finally, numerical results based on the probability of detection and false alarm versus SNR or the number of samples are presented to validate the performance of the proposed scheme.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.335-344
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• 2001

Texture is extensively studied in a variety of image processing applications such as image segmentation and classification because it is an important property to perceive regions and surfaces. This paper focused on the analysis and synthesis of textured single and multiband images using Markov Random Field model considering the existent spatial correlation. Especially, for multiband images, the cross-channel correlation existing between bands as well as the spatial correlation within band should be considered in the model. Although a local interaction is assumed between the specified neighboring pixels in MRF models, during the maximization process, short-term correlations among neighboring pixels develop into long-term correlations. This result in exhibiting phase transition. In this research, the role of temperature to obtain the most probable state during the sampling procedure in discrete Markov Random Fields and the stopping rule were also studied.

• ETRI Journal
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• v.34 no.6
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• pp.892-899
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• 2012

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing-time joint detection framework. This framework detects a wideband M-ary quadrature amplitude modulation (M-QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M-QAM than for binary phase-shift keying or any real modulation scheme.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1645-1653
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• 2014

In general, RGB, NIR, and thermal images are used for obtaining geospatial data. Such multiband images are collected via devices mounted on satellites or manned flights, but do not always meet users' expectations, due to issues associated with temporal resolution, costs, spatial resolution, and effects of clouds. We believe high-resolution, multiband images can be obtained at desired time points and intervals, by developing a payload suitable for a low-altitude, auto-piloted UAV. To achieve this, this study first established a low-cost, high-resolution multiband image collection system through developing a sensor and a payload, and collected geo-referencing data, as well as RGB, NIR and thermal images by using the system. We were able to obtain a 0.181m horizontal deviation and 0.203m vertical deviation, after analyzing the positional accuracy of points based on ortho mosaic images using the collected RGB images. Since this meets the required level of spatial accuracy that allows production of maps at a scale of 1:1,000~5,000 and also remote sensing over small areas, we successfully validated that the payload was highly utilizable.

• ETRI Journal
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• v.32 no.2
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• pp.214-221
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• 2010

Radio frequency (RF) subsampling can be used by radio receivers to directly down-convert and digitize RF signals. A goal of a cognitive radio/software defined ratio (CR/SDR) receiver design is to place the analog-to-digital converter (ADC) as near the antenna as possible. Based on this, a band-pass sampling (BPS) frontend for CR/SDR is proposed and verified. We present a receiver architecture based second-order BPS and signal processing techniques for a digital RF frontend. This paper is focused on the benefits of the second-order BPS architecture in spectrum sensing over a wide frequency band range and in multiband receiving without modification of the RF hardware. Methods to manipulate the spectra are described, and reconstruction filter designs are provided. On the basis of this concept, second-order BPS frontends for CR/SDR systems are designed and verified using a hardware platform.

• Korean Journal of Remote Sensing
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• v.7 no.2
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• pp.149-163
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• 1991

This paper discusses the capabilities of airborne remotely sensed data to detect and classify forest damades. In this work the AMS (Aircraft Multiband Scanner) was used to obtain digital imagery at 300m altitude for forest damage inventory in the Black Forest of Germany. MSS(Multispectral Scanner) digital numbers were converted to spectral emittance and radiance values in 8 spectral bands from the visible to the thermal infrared and submitted to a maximum-likelihood classification for : (1) tree species ; and. (2) damage classes. As expected, the resulted, the results of MSS data with high spatial resolution 0.75m$\times$0.75m enabled the detection and identification of single trees with different damages and were nearly equivalent to the truth information of ground checked data.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.63-87
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• 1985

Considerable experience has been reported on the use of spectral data to measure the canopy biomass of dryland grain crops and the use of these estimates to forecast subsequent grain yield. These basic procedures were retested to assess the use of the general process to forecasting grain yield for paddy rice. The use of the ratio of a multiband radiometer simulation of Thematic Mapper band 4(.76 to .90 .mu.m) divided by band 3 (.63 to .69 .mu.m) was tested to estimate the canopy biomass of paddy rice as a function of the stage of development of the rice. The correlation was found to be greatest (R = .94) at panicle differentiation about midway through the development cycle of the rice canopy. The use of this ratio of two spectral bands as a surrogate for canopy biomass was then tested for its correlation against final grain yield. These spectral estimates of canopy biomass produced the highest correlations with final grain yield (R = .87) when measured at the canopy development stages of panicle differentiation and heading. The impact of varying the amounts of supplemental nitrogen on the use of spectral measuremants of canopy biomass to estimate grain yield was also determined. The effect of the development of a significant amount of weed biomass in the rice canopy was also clearly detected.

• Journal of Astronomy and Space Sciences
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• v.32 no.2
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• pp.161-166
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• 2015

Analysis of lunar samples returned by the US Apollo missions revealed that the lunar highlands consist of anorthosite, plagioclase, pyroxene, and olivine; also, the lunar maria are composed of materials such as basalt and ilmenite. More recently, the remote sensing approach has enabled reduction of the time required to investigate the entire lunar surface, compared to the approach of returning samples. Moreover, remote sensing has also made it possible to determine the existence of specific minerals and to examine wide areas. In this paper, an investigation was performed on the reflectance distribution and its trend. The results were applied to the example of the double ray stretched in parallel lines from the Tycho crater to the third-quadrant of Mare Nubium. Basic research and background information for the investigation of lunar surface characteristics is also presented. For this research, resources aboard the SELenological and ENgineering Explorer (SELENE), a Japanese lunar probe, were used. These included the Multiband Imager (MI) in the Lunar Imager/Spectrometer (LISM). The data of these instruments were edited through the toolkit, an image editing and analysis tool, Exelis Visual Information Solution (ENVI).

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.