一种结合形状特征的多光谱遥感图像分割方法

提出一种结合形状特征和光谱特征对多光谱遥感图像进行分割的方法。首先确定形状因子和光谱因子的表达式．然后应用区域生长算法在给定区域异质性阈值的约束条件下进行图像分割。实验结果表明此方法对中高分辨率多光谱遥感图像的分割效果较好，有一定的推广价值。     

应用遥感影像与 WebGIS 建立林场级森林资源管理信息系统

以湖南省林业科学院试验林场为研究区域,以森林资源野外调查数据、ETM+遥感影像数据和基础地理信息数据为研究资料,通过对林场内森林资源数据的提取、系统功能需求分析和搭建系统所需的软硬件环境,建立起基于 B/S 结构的 WebGIS 林场级森林资源管理信息系统,实现森林资源信息的管理、查询、编辑、分析、统计等功能,达到森林资源管理的目的。     

基于多标准分析方法的流域管理机制构建

流域是地球淡水循环的重要部分。为应对全球性缺水危机必须对流域进行有效管理。首先分析了流域管理的内涵,其次利用多标准分析方法建立了以可持续发展为目标的流域管理体系,最后指出流域管理中应该注意的问题。     

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

Crop injury caused by off-target drift of herbicide can seriously reduce growth and yield and is of great concern to farmers and aerial applicators. Farmers can benefit from identifying an indirect method for assessing the level of crop injury. This study evaluates the combined use of statistical methods and vegetation indices (VIs) derived from multispectral images to assess the level of crop injury. An experiment was conducted in 2009 to determine glyphosate injury differences among the cotton, corn, and soybean crops. The crops were planted in eight rows spaced 102 cm apart and 80 m long with four replications. Seven VIs were calculated from multispectral images collected at 7 and 21 days after the glyphosate application (DAA). At each image collection date, visual injury estimates were assessed and data were collected for plant height, chlorophyll content, and shoot dry weight. From the seven VIs evaluated as surrogate for glyphosate injury identification using a canonical correlation analysis (CCA), the Chlorophyll Vegetation Index (CVI) showed the highest correlation with field-measured plant injury data. CVI image values were subtracted from the CVI average values of the non-injured area to generate CVI residual images (CVI_res). Frequency distribution histograms of CVI_res image values were calculated to assess the level of injury between crops. These data suggested that injury increased from 7DAA to 21DAA with corn exhibiting higher severity of injury than cotton or soybean, while only moderate injury was observed for cotton. The techniques evaluated in this study are promising for estimating the level of glyphosate herbicide drift, which can be used to make appropriate management decisions considering crop proximity.     

Mapping Potential Crop Management Zones within Fields: Use of Yield-map Series and Patterns of Soil Physical Properties Identified by Electromagnetic Induction Sensing

Investment in precision farming technologies can be expensive and is not expected to be cost-effective for every farm. Previous research and farm experience has shown that the amount of soil variability across a farm and within a field is of key importance for determining potential benefits from the adoption of precision farming. The research reported here evaluates the analysis of yield map sequences and electromagnetic induction (EMI) soil sensing as potentially cost-effective methods for identifying and mapping soil-determined management zones within fields. Both methods are shown to provide useful information for the provisional delineation of soil type boundaries and crop management zones, though soil examination in the field is still necessary to confirm specific soil characteristics.     

Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry

This paper describes a computer vision based model for object detection that can serve as a preliminary step in fruit prognosis, which involves the estimation of the number, diameter and yield of apple fruits. In order to overcome the recognition unreliability in uncontrolled environments caused by uneven illumination conditions, partly occluded surfaces, and similar background features, we rely on a combination of the object's colour, texture and 3D shape properties. In our research, we apply colour segmentation to multiple scene snapshots to separate potential regions from the background and verify them first with texture analysis and second by reconstructing them to 3D space. By analysing all three distinct features (colour, texture and 3D shape) of possible areas, we can safely conclude if they represent fruits we are looking for. Once we detect and verify all areas representing fruits, we can measure their size and model estimated fruit yield.     

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor.Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.     

遥感干旱监测数据处理系统的设计

遥感技术是干旱监测的有效途径之一，但遥感影像数据量大，如何高效、快速地处理影像数据是必须解决的问题。本文结合软件工程的相关思想，阐述了遥感干旱监测中影像数据处理系统的设计方法与过程。     

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.     

Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia

The Russian wheat aphid, Diuraphis noxia, is an important pest of winter wheat, Triticum aestivum, and barley, Hordeum vulgare that has caused an annual economic loss estimated at over 1 billion dollars since it first appeared in the United States. The objective of this study was to determine the potential of combining multispectral imagery with spatial pattern recognition to identify and spatially differentiate D. noxia infestations in wheat fields. Multispectral images were acquired using an MS3100-CIR multispectral camera. D. noxia, drought, and agronomic conditions were identified as major causes for stresses found in wheat fields. Seven spatial metrics were computed for each stress factor. The analysis of spatial metrics quantitatively differentiated the three types of stress found within wheat fields. Detection and differentiation of wheat field stress may help in mapping stress and may have implications for site-specific monitoring systems to identify D. noxia infestations and help to target pesticide applications.     

Textural analysis of soil images to quantify and characterize the spatial variation of soil properties using a real-time soil sensor

The primary aim of this work was to predict soil moisture content and soil organic matter using soil image texture statistics. Co-occurrence method texture statistics were used to characterize Andisol soils to extend the possibility of using RGB color space in representing composite soil color. Four co-occurrence method textural features; angular second moment (ASM), contrast (CON), correlation (COR) and inverse difference moment (IDM) calculated from generalized matrix for image texture representation were used to describe soil moisture content variation under laboratory conditions. It was found that CON and COR had negative responses to moisture content (MC) and ASM had positive response to MC. The same were also observed in direct captured field soil images in terms of textural indices against MC and soil organic matter (SOM). The correlations were significant for ASM and COR in fertilizer and combined (fertilizer-manure) plots and insignificant in manure plots. To relate sub-surface image textural indices and soil properties for individual years, stepwise multiple linear regression (SMLR) and supervised feed-forward neural networks (NN) were investigated in an attempt to provide minimal prediction errors. The improvements achieved by NN with minimal prediction errors were better than SMLR in different years. It was assumed that several years of data sets with a much larger number of observations could be used to differentiate fundamental soil properties.     

Method of evaluating diversity of carrot roots using a self-organizing map and image data

Carrot cultivars differ in nutritional value, and the quality of individual pieces may differ from the average, which can be troublesome for companies that process carrots. What is needed is a tool for quick confirmation of the existence and definition of the nature of the differences between the carrot roots. The aim of the experiment was to test whether the simple image parameters, such as colour, will determine the variation within populations of carrot and what kind of information about this differentiation may be provided by a self-organizing map. We indicated that the image data in combination with the self-organization algorithm can provide useful information about carrot roots. However analysis of each individual neural neuron could produce too many summaries to be useful. Therefore, the map was segmented into clusters using k-means method. Segmentation of the self-organizing map allowed to make a comprehensive evaluation of the roots. From the practical point of view such segmentation could help in sorting material, for example, taking into account suitability for processing. The proposed method is sensitive to changes in the processing features of the raw material and is able to locate it in the appropriate area of the topological map. This makes the processor can quickly check whether the raw material he received meets the standards established by a food processing plant. This information is important for the processing industry, in which final product parameters depend on the quality of raw material.     

高校经管类学生参与科研意愿的影响因素分析——基于安徽省8所高校的问卷调查

大学生参与科研是高校培养双创型人才的重要途径之一，高校经管类专业学生培养存在重理论、轻实践的现象。安徽省8所高校1 406名经济管理类学生科研意愿的实证分析表明：年级、学习成绩、科研重要性认知、科研了解程度和学校科研活动开展对大学生科研意愿有显著影响。高校在经管类学生培养过程中应通过加强科研宣传推广、开设科研训练课程、丰富科研活动多样性、激励教师科研指导和开拓校外实践教学基地建设等措施增强学生参与科研的积极性，提升学生的双创能力。