流动成像测井研究进展

流动成像测井属于对非均匀介质动态的非线性测量，通过对油气井内多相流体信息的投影扫描和反演处理，实时成像显示流体分布及流动状况，国外研制的电导法，电容法仪器采用电导探针或电容元件构成阵列测量探头，分别利用油气与水的导电特性和介电特性差异辨识井内流体，对流动截面的测量局限于个别点上，已在水平井中见到应用效果，但对物场信息投影测量的数据量和分辨率未能满足成像要求。我国提出的电磁法采用环状阵列电极构成测量探头，综合利用油气与水的导电和介电特性差异辨识井内流体，可以测量获得多相流体流动截面的清晰图像，为研究开发全新的流动成像测井系统奠定了良好基础。     

RS与GIS集成环境下的遥感图像分析技术

通过多源数据融合，将目前GIS中包含的地理信息作为辅助数据，引入到RS图像分析过程中。GIS数据在空间信息、目标属性和目标范围等方面对RS图像分析具有重要的引导作用。     

利用TM数据快速提取含矿蚀变带方法研究

热液蚀变岩类是在含矿热液作用下引起原岩化学成分和物理性质发生变化的结果，因此蚀变岩类往往与内生产矿产伴生，有时其本身就是含矿体。不同蚀变矿特级组合具有独特的光谱响应，但是光谱的反射和吸收产特征较弱称为遥感找矿弱信息标志。     

图象分割中随机噪音的影响

该文在生成的灰度图象上加上随机噪音，用最大后验（ＭＡＰ）估计方法、模糊Ｃ－均值方法和Ｏｔｓｕ阈值方法进行了分割计算，对结果进行了对比分析，绘出了分割随噪音的标准差变化曲线，对噪音图象使用了不同的滤波方法处理之后，并进行计算。     

影像匹配中金字塔数据结构比较与快速算法

分析了多种金字塔数据结构的生成方法,比较了不同方法在影像匹配中的优劣,并提出了一种影像金字塔生成的快速算法。     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.     

地图复制印刷中图像还原定律的研究

根据地图复制印刷中图像变换的模型,推导了若干图像还原定律和推论,并给出数学证明,从而使地图复刊印刷中的相关经验知识,上升为理论的高度。     

Shape indexes for semi-automated detection of windbreaks in thematic tree cover maps from the central United States

Windbreaks are an important ecological resource across the large expanse of agricultural land in the central United States and are often planted in straight-line or L-shaped configurations to serve specific functions. As high-resolution (i.e., <5 m) land cover datasets become more available for these areas, semi- or fully-automated methods for distinguishing windbreaks from other patches of trees are needed for use with thematic raster datasets. To address this need, we created three shape indexes: a morphology-based index that we have named the Straight and Narrow Feature Index (SNFI), a windbreak sinuosity index, and an area index indicating the occupied fractional area of a bounding box. The indexes were tested in two study areas: (1) a riparian area dominated by sinuous bands of trees but mixed with row crop agriculture and (2) an agricultural area with a mix of straight-line and L-shaped windbreaks. In the riparian area, a Kruskall–Wallis rank sum test indicated class differences for all three indexes, and pairwise comparisons indicate windbreaks and riparian trees are separable using any of the three indexes. SNFI also produced significant differences between windbreaks oriented in different directions (east–west vs. north–south). In the agricultural area, the Kruskall–Wallis rank sum test indicated differences between classes for all three indexes, and pairwise comparisons show that all class pairs have significant differences for at least one index, with the exception of L-shaped windbreaks vs. non-windbreak tree patches. We also used classification trees to objectively assign representative samples of tree patches to classes using both single indexes and multiple indexes. Classes were correctly assigned for more than 90% of the samples in both the riparian and agricultural study areas. In the riparian area, combining indexes did not improve accuracy compared to using SNFI alone, whereas in the agricultural area, combining the three indexes produced the best result. Thematic datasets derived from high-resolution imagery are becoming more available, and extracting useful information can be a challenge, partly due to the large amount of data to assess. Calculating the three shape indexes presented can assist with efficient identification of candidate windbreaks and as such, hold good promise for value-added analysis of tree function in the central United States.