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  • Articles  (480)
  • 2015-2019  (480)
  • 2016  (480)
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  • 2015-2019  (480)
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  • 1
    Publication Date: 2016-12-31
    Description: The carbon dioxide (CO 2 ) emissions released from biomass burning significantly affect the temporal variations of atmospheric CO 2 concentrations. Based on a long-term (July 2009–June 2015) retrieved data sets by the greenhouse gases observing satellite (GOSAT), the seasonal cycle and interannual variations of column-averaged volume mixing ratios of atmospheric carbon dioxide (XCO 2 ) in four fire affected continental regions were analyzed. The results showed that Northern Africa (NA) had the largest seasonal variations after removing its regional trend of XCO 2 with peak-to-peak amplitude of 6.2 ppm within the year, higher than central South America (CSA) (2.4 ppm), Southern Africa (SA) (3.8 ppm), and Australia (1.7 ppm). The detrended regional XCO 2 ( $triangle $ XCO 2 ) was found to be positively correlated with the fire CO 2 emissions during the fire activity period but with different seasonal variabilities. NA recorded the largest change of seasonal variations of $triangle $ XCO 2 with a total of 12.8 ppm during fire seasons, higher than CSA, SA, and Australia with 5.4, 6.7, and 2.2 ppm, respectively. During the fire episode, the positive $triangle $ XCO 2 was noticed during June–November in CSA, December to next June in NA, and May–November in SA. The Pearson correlation coefficients between the variations of $triangle $ XCO 2 and fire CO 2 emissions achieved the best correlations in SA ( $R = 0.77$ and $p 〈 0.05$ ). This letter revealed that fire CO 2 emissions and GOSAT XCO 2 presented consistent seasonal variations.
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  • 2
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    Unknown
    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-12-31
    Description: This letter presents a novel deep learning algorithm for feature extraction from the hyperspectral images. The proposed method takes advantage of the knowledge that the features of the spatial-spectral data naturally fall into an array of groups with respect to different spectral bands. Aiming to reduce the influence of redundant spectral bands adaptively using unlabeled hyperspectral data, we incorporate the group information in the training algorithm of the deep neural network via a regularized weight-decay process. Experiments over different benchmarks of hyperspectral images show that the proposed method provides competitive solution with the state-of-the-art approaches.
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  • 3
    Publication Date: 2016-12-31
    Description: Block kriging (BK) is a common method of predicting the true value at the pixel scale when validating remote sensing retrieval products. However, measurement errors (MEs) increase the prediction uncertainty. In this letter, an extended interpolation technique—BK with MEs (BKMEs)—is developed. The properties of BKME are proven through derivation and demonstrated in a case study of soil moisture (SM) upscaling. Three prediction scenarios—one without MEs (BK), BK with homogeneous MEs (BKHOME), and BK with heterogeneous MEs (BKHEME)—are considered for the upscaling of SM data observed by a distributed wireless sensor network, and the results are compared. Both BK and BKHOME yield the same upscaling results, which differ from those of BKHEME, and the prediction results of BKHEME show less bias than those of the other scenarios. Because both BKHOME and BKHEME consider MEs, their prediction results show smaller kriging variances than do the BK results. Three primary conclusions are drawn. The first is that the optimal kriging coefficients assigned to the observations are affected not only by spatial distance but also by the MEs when the MEs of the samples are unequal. The second is that when the MEs are equal, it may not be necessary to consider the MEs to predict the value for an unobserved location. The third is that although the prediction uncertainty can be reduced by considering MEs, it is more meaningful to consider unequal MEs than equal MEs in the prediction process. BKME is an advanced upscaling method that achieves improved prediction accuracy by considering MEs.
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  • 4
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-12-31
    Description: The Ku-band Oceansat-2 Scatterometer (OSCAT) is very similar to the Quick Scatterometer (QuikSCAT), which operated from 1999 to 2009. OSCAT continues the Ku-band scatterometer data record through 2014 with an overlap of 19 days with QuikSCAT’s mission in 2009. This letter discusses a particular climate application of the time series for sea ice extent observation. In this letter, a QuikSCAT sea ice extent algorithm is modified for OSCAT. Gaps in OSCAT data are accounted for and filled in to support sea ice extent mapping. The OSCAT sea ice extent data are validated with QuikSCAT and Special Sensor Microwave/Imager sea ice extent data.
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  • 5
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-12-31
    Description: In this letter, an accurate mathematical model for azimuth ambiguity in stripmap synthetic aperture radar (SAR) images is first constructed, with an azimuth ambiguity factor (AAF) defined as the residual amplitude and phase terms of ambiguities. Next, a novel framework for reconstructing and suppressing azimuth ambiguity is proposed based on the analysis of the AAF. In this framework, azimuth ambiguities are accurately reconstructed by applying reconstruction filters in the range Doppler and 2-D frequency domain, and then, the reconstructed signal is used for suppressing azimuth ambiguities. Moreover, the proposed framework does not depend on the statistical characteristics of a SAR image and is capable of reducing the space-variant ambiguities. As verified by both simulated data and real TerraSAR-X data, the proposed method is capable of suppressing azimuth ambiguities in SAR images.
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  • 6
    Publication Date: 2016-12-31
    Description: A regression-based downscaling of land surface temperature was developed over the heterogeneous urban area of Aprilia, Central Italy, using high resolution (HR) airborne data. Airborne sensors provided thermal and visible–near infrared (VNIR) measurements at 2-m pixel size. Coarse resolution images at 40, 30, and 20 m, upscaled by aggregation from the native airborne data, were sharpened to the finer resolution of 2 m. The main core of the downscaling method is the use of the spectral mixture analysis (SMA) to derive fractional pixel composition as predictors of the regression scheme. The HR VNIR data allow choosing detailed land cover types in the application of SMA, such as bright/dark roofs, and the benefit of this detailed selection is proved. The estimation error of the custom technique improves of about 10%–15% with respect to a classical regression downscaling.
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  • 7
    Publication Date: 2016-12-31
    Description: Target recognition in synthetic aperture radar (SAR) images has become a hotspot in recent years. The backscattering characteristic of target is a significant issue taken into consideration in SAR applications. Almost all of the previous work focus on the scatter point extraction to depict the backscattering characteristic of the target; however, a point-target corresponds to a region rather than a single point due to the convolution during the imaging. Based on this fact, we first analyze the extent to how a point-target spreads, then propose a novel scatter cluster extraction (SCE) method, and utilize the scatter cluster as the feature to solve the airplane recognition problem in SAR images. In practice, there often exist interfering objects near the target to be classified. To overcome this issue, we design a reweighted sparse representation (RSR)-based automatic purifying method by assigning a weight to each element of the feature iteratively according to the representation error. Since the element with large representation error always corresponds to the interfering objects, we give it a small weight, consequently suppressing the influence of the interference. Experimental results demonstrate that the proposed SCE method outperforms the traditional scatter point extraction-based method as well as some state-of-the-art methods. The comparison result also validates the effectiveness of the proposed RSR method.
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  • 8
    Publication Date: 2016-12-31
    Description: High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand. Ultrawideband (UWB) millimeter-wave radars are one of the most promising devices for the above-mentioned applications. For target recognition using sensors, it is necessary to convert observational data into full 3-D images with both time efficiency and high accuracy. For such conversion algorithm, we have already proposed the range points migration (RPM) method. However, in the existence of multiple separated objects, this method suffers from inaccuracy and high computational cost due to dealing with many observed RPs. To address this issue, this letter introduces Doppler-based RPs clustering into the RPM method. The results from numerical simulations, assuming 140-GHz band millimeter radars, show that the addition of Doppler velocity into the RPM method results in more accurate 3-D images with reducing computational costs.
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  • 9
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-12-31
    Description: Time–frequency (TF) analysis can reveal local variations in seismic data processing and interpretation, where seismic signals are nonstationary and time varying. High-quality TF representation (TFR) is important for revealing the local information about these nonstationary seismic signals and describing geological structures. Due to the Heisenberg uncertainty principle, traditional TF methods (e.g., short time Fourier transform and continuous wavelet transform) cannot get the finest time resolution and the best frequency resolution at the same time, which leads to ambiguous TFR with a negative effect on the seismic signal analysis. Concentration in frequency and time is proposed to distinguish the different TF contents of time-dependent signals with time-varying amplitude and instantaneous frequencies. We introduce this promising TF analysis tool to seismic data processing. Experiments on synthetic signals and seismic data show its validity and effectiveness, which is helpful for seismic data interpretation in the future.
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  • 10
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2016-12-31
    Description: The altitude information of single remote sensing image may aid in detecting natural disasters such as landslides or mud-rock flow. Accordingly, in this letter, an approach based on dark channel prior is proposed for the altitude extraction of single remote sensing image, and it also overcomes the effect of mountain shadow. We first detect the mountain shadows based on machine learning, and then adjust the brightness of each shadow with an adaptive adjustment parameter. Next, we estimate the altitude information based on dark channel prior, including atmospheric light calculation and soft matting. The experimental results with real mountain remote sensing images demonstrate that the proposed algorithm can obtain the accurate relative altitude information, which is effective for the extraction of the relative altitude of single mountain remote sensing image with shadows.
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