TETRA通信与干扰信号智能识别技术

陆上集群无线电(Terrestrial Trunked Radio,TETRA)数字集群通信系统因其开放性易受到内外部电磁干扰。干扰信号的类型多种多样,针对不同的干扰样式,采取的抗干扰措施也各不相同,因此干扰信号的识别具有重大意义。基于此,提出了一种干扰信号智能识别技术。该技术首先对受到不同干扰后的TETRA音频数据进行特征提取,并筛选出具有分类能力的特征,其次使用决策树、支持向量机和随机森林3种分类模型对特征提取后的待测试信号进行智能分类识别。实验结果表明,使用的这3种模型能够有效判断TETRA系统中的信号是否受到干扰,以及受到何种样式的干扰,可为后续TETRA系统中的信号干扰识别提供参考。     

Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval

An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R₇₈₀ − R₇₁₀] / [R₇₈₀ − R₆₈₀]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived.     

Remote sensing of structural complexity indices for habitat and species distribution modeling

Spatial distribution models are increasingly used in ecological studies, but are limited by the poor accuracy of remote sensing (RS) for mapping microhabitat (< 0.1 ha) features. Mapping accuracy can be improved by combining advanced RS image-processing techniques with microhabitat data expressed as a structural complexity index (SCI). To test this idea, we used principal components analysis (PCA) and an additive SCI method developed for forest ecology (calculated by re-scaling and summing representative structural variables) to summarize 13 microhabitat-scale (0.04 ha) vegetation structure attributes describing the rare mountain bongo antelope's (Tragelaphus eurycerus isaaci) habitat in Kenya's Aberdare mountains. Microhabitat data were collected in 127 plots: 37 related to bongo habitat use, 90 from 1 km-spaced grid points representing overall habitat availability and bongo non-presence. We then assessed each SCI's effectiveness for discerning microhabitat variability and bongo habitat selection, using Wilcoxon Rank Sum tests for differences in mean SCI scores among plots divided into 4 vegetation classes, and the Area Under the Curve (AUC) of receiver operating characteristics from logistic regressions. We also examined the accuracy of predicted SCI scores resulting from regression models based on variables derived from a) ASTER imagery processed with spectral mixture and texture analysis, b) an SRTM DEM and c) rainfall data, using the 90 grid plots for model training and the bongo plots as an independent test dataset. Of the five SCIs derived, two performed best: the PCA-derived Canopy Structure Index (CSI) and an additive index summarizing 8 structural variables (AI₈). CSI and AI₈ showed significant differences between 5 of 6 vegetation class pairs, strong abilities to distinguish bongo-selected from available habitat (AUCs = 0.71 (CSI); 0.70 (AI₈)), and predicted scores 60-110% more accurate than reported by other studies using RS to quantify individual microhabitat structural attributes (CSI model R² = 0.51, RMSE = 0.19 (training) and 0.21 (test); AI₈ model R² = 0.46, RMSE = 0.17 (training) and 0.19 (test)). Repeating the Wilcoxon tests and logistic regressions with RS-predicted SCI values showed that AI₈ most effectively preserved the patterns found with the observed SCIs. These results demonstrate that SCIs effectively characterize microhabitat structure and selection, and boost microhabitat mapping accuracy when combined with enhanced RS image-processing techniques. This approach can improve distribution models and broaden their applicability, makes RS more relevant to applied ecology, and shows that processing field data to be more compatible with RS can improve RS-based habitat mapping accuracy.     

环境减灾卫星影像森林火灾监测技术方法研究

森林火灾是世界性的、频繁发生的重大自然灾害。随着国内外航天科技的迅猛发展,卫星遥感技术特别是红外卫星遥感已成为森林火灾监测的一种有效手段。我国的环境一号卫星A、B星(简称HJ-1A卫星、1B卫星)于2008年9月成功发射,其中的HJ-1B卫星搭载了红外多光谱相机,在森林火灾监测方面具有得天独厚的优势,可在早期的火灾发现、中期的灾害跟踪、后期的灾害损失评估中发挥重要作用。本文主要分析了环境减灾卫星在森林火灾监测方面的优势,对环境减灾卫星森林火灾监测技术和方法进行了研究。     

遥感图像林区自动阈值分割算法及性能比较

选取了几种经典的自动阈值选取算法对高分辨率遥感图像林区目标进行分割实验,并引入错分类误差、形状测度、均匀测度、最终测量精度和运算速度作为算法评判准则,客观、定量地比较了各种算法对高分辨率遥感图像林区目标的分割性能,所得结论对林区目标分割方法的选取具有一定的指导作用。     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Estimating Siberian timber volume using MODIS and ICESat/GLAS

Geosciences Laser Altimeter System (GLAS) space LiDAR data are used to attribute a MODerate resolution Imaging Spectrometer (MODIS) 500 m land cover classification of a 10° latitude by 12° longitude study area in south-central Siberia. Timber volume estimates are generated for 16 forest classes, i.e., four forest cover types × four canopy density classes, across this 811,414 km² area and compared with a ground-based regional volume estimate. Two regional GLAS/MODIS timber volume products, one considering only those pulses falling on slopes ≤ 10° and one utilizing all GLAS pulses regardless of slope, are generated. Using a two-phase(GLAS-ground plot) sampling design, GLAS/MODIS volumes average 163.4 ± 11.8 m³/ha across all 16 forest classes based on GLAS pulses on slopes ≤ 10° and 171.9 ± 12.4 m³/ha considering GLAS shots on all slopes. The increase in regional GLAS volume per-hectare estimates as a function of increasing slope most likely illustrate the effects of vertical waveform expansion due to the convolution of topography with the forest canopy response. A comparable, independent, ground-based estimate is 146 m³/ha [Shepashenko, D., Shvidenko, A., and Nilsson, S. (1998). Phytomass (live biomass) and carbon of Siberian forests. Biomass and Bioenergy, 14, 21-31], a difference of 11.9% and 17.7% for GLAS shots on slopes ≤ 10° and all GLAS shots regardless of slope, respectively. A ground-based estimate of total volume for the entire study area, 7.46 × 10⁹ m³, is derived using Shepashenko et al.'s per-hectare volume estimate in conjunction with forest area derived from a 1990 forest map [Grasia, M.G. (ed.). (1990). Forest Map of USSR. Soyuzgiproleskhoz, Moscow, RU. Scale: 1:2,500,000]. The comparable GLAS/MODIS estimate is 7.38 × 10⁹ m³, a difference of less than 1.1%. Results indicate that GLAS data can be used to attribute digital land cover maps to estimate forest resources over subcontinental areas encompassing hundreds of thousands of square kilometers.     

深度随机森林在离网预测中的应用

在电信运营商领域,离网预测模型是企业决策者用来发现潜在离网用户(即停用运营商服务)的主要手段。目前离网预测模型都是基于逻辑回归、决策树、神经网络及随机森林等浅层机器学习算法,但是在大数据的背景下,这些浅层算法在预测问题上很难取得更高的精度。因此,提出了一种新型的深层结构模型——深度随机森林,通过将传统浅层随机森林堆积成深层结构模型,获得更高的预测精度。在运营商真实数据上进行了大量实验,结果证明深层随机森林模型比传统浅层机器学习算法在离网预测问题上可以得到更好的效果。同时,增大训练数据量可以进一步提升深层随机森林的预测能力,从而证明了在大数据环境下深层模型的潜力。     

基于颜色和纹理特征的林火烟雾识别

为了实现森林火灾的智能化预警,提出了基于颜色和纹理特征的林火烟雾识别方法.首先使用颜色特征确定烟雾疑似区域,随后采用局部二值模式方差(Local Binary Pattern Variance,LBPV)提取疑似区域纹理的不规则度特征并产生LBP图像.然后利用小波变换分解LBP图像并提取模糊度、复杂度和相关度特征.最后利用支持向量机(Support Vector Machine,SVM)进行烟雾识别.结果证明,颜色结合纹理特征方法可以有效实现林火烟雾的识别,为林火烟雾识别研究提供了一种有效方案.     

一种改进的SVM算法在乳腺癌诊断方面的应用

针对计算机辅助诊断(CAD)技术在乳腺癌疾病诊断准确率的优化问题,提出了一种基于随机森林模型下Gini指标特征加权的支持向量机方法(RFG-SVM)。该方法利用了随机森林模型下的Gini指数衡量各个特征对分类结果的重要性,构造具有加权特征向量核函数的支持向量机,并在乳腺癌疾病诊断方面加以应用。经理论分析和实验数据验证,相比于传统的支持向量机(SVM),该方法提升了分类预测的性能,其结果与最新的方法相比也具有一定的竞争力,而且在医疗诊断应用方面更具优势。