结合深度学习和图割法的遥感影像建筑物检测     

刘舸  邓兴升 《测绘通报》2019,(11):69-73

提出一种基于卷积神经网络和图割法的自动提取高分影像建筑物的方法。首先，通过卷积神经网络定位与检测建筑物的位置，逐一提取单个建筑物轮廓，利用检测结果分别建立建筑物和非建筑物的高斯混合模型（GMM），然后结合最大流最小割的图像分割方式实现全局优化，完成建筑物初步提取，最后用形态学进行优化。通过试验证明了该方法的可行性。  相似文献   

深度学习GoogleNet模型支持下的中分辨率遥感影像自动分类     

陈斌  王宏志  徐新良  王首泰  张亚庆 《测绘通报》2019,(6):29-33,40

提出了一种基于深度学习技术的遥感分类方法，它能有效解决中分辨率影像在分类过程中出现的像元混分问题。研究选用2016年5月12日武汉市Landsat 7 ETM+遥感影像，基于GoogleNet模型中的Inception V3网络结构，借助迁移学习方法，构建出遥感分类模型，实现了对武汉市主城区4类典型地物（不透水层、植被、水体和其他用地）的自动分类提取，并将分类结果与传统最大似然分类（ML）结果进行了对比分析。研究表明：基于深度学习方法的遥感影像总体分类精度高达88.33%，Kappa系数为0.834 2，明显优于传统ML方法总体分类精度83%和Kappa系数0.755 0，而且有效抑制了地物在分类过程中出现的像元混分现象。  相似文献   

结合主动学习和词袋模型的高分二号遥感影像自动化分类     

张金盈  姚光虎  林琳  郭怀轩 《测绘通报》2019,(2):103-107

高分卫星遥感影像空间分辨率的提高，使得地物的光谱和纹理变得更加丰富和复杂，这给遥感影像的自动化分类带来严重挑战。因此，本文提出了一种结合主动学习和词袋模型的高分二号遥感影像分类方法。首先，对研究区域进行多尺度分割，建立影像分割对象集；然后，采用词袋模型构建影像对象的语义特征向量；最后，充分考虑位于分类边界的不确定性样本分布，迭代选择最优样本用于训练支持向量机，用于分类遥感影像。为了验证本文方法的有效性和稳健性，以山东省某市的高分二号遥感影像为试验数据进行了试验分析。结果表明，本文提出的方法可以有效地将研究区域分为水体、地面、植被和建筑物四类，正确率达到90.6%以上。  相似文献   

新工科理念下的实验课程教学改革与实践探索     

蔡剑红  霍亮  朱凌 《测绘通报》2019,(2):147-152

近年来为适应创新人才的培养，"卓越计划"开启了全新教学模式。基于新工科理念，为了更好地进行实验课程教学，以"3ds Max软件三维建模"为例，笔者从课程内容安排、教学方式、考核机制等方面进行了教学探索和实践。本文以应用软件实验类课程项目教学的实践经验为基础，注重学生团队合作和交流能力、实践能力、创新能力培养，以及多渠道快速学习新事物的能力，通过与测绘新技术下多软件融合，使学生拥有知识整合能力，为提高实践教学提供参考和借鉴，以更好地适应新工科理念下的创新人才培养。  相似文献   

Strain analysis of a seismically imaged mass‐transport complex,offshore Uruguay     

Michael J. Steventon  Christopher A.‐L. Jackson  David M. Hodgson  Howard D. Johnson 《Basin Research》2019,31(3):600-620

Strain style, magnitude and distribution within mass‐transport complexes (MTCs) are important for understanding the process evolution of submarine mass flows and for estimating their runout distances. Structural restoration and quantification of strain in gravitationally driven passive margins have been shown to approximately balance between updip extensional and downdip contractional domains; such an exercise has not yet been attempted for MTCs. We here interpret and structurally restore a shallowly buried (c. 1,500 mbsf) and well‐imaged MTC, offshore Uruguay using a high‐resolution (12.5 m vertical and 15 × 12.5 m horizontal resolution) three‐dimensional seismic‐reflection survey. This allows us to characterise and quantify vertical and lateral strain distribution within the deposit. Detailed seismic mapping and attribute analysis shows that the MTC is characterised by a complicated array of kinematic indicators, which vary spatially in style and concentration. Seismic‐attribute extractions reveal several previously undocumented fabrics preserved in the MTC, including internal shearing in the form of sub‐orthogonal shear zones, and fold‐thrust systems within the basal shear zone beneath rafted‐blocks. These features suggest multiple transport directions and phases of flow during emplacement. The MTC is characterised by a broadly tripartite strain distribution, with extensional (e.g. normal faults), translational and contractional (e.g. folds and thrusts) domains, along with a radial frontally emergent zone. We also show how strain is preferentially concentrated around intra‐MTC rafted‐blocks due to their kinematic interactions with the underlying basal shear zone. Overall, and even when volume loss within the frontally emergent zone is included, a strain difference between extension (1.6–1.9 km) and contraction (6.7–7.3 km) is calculated. We attribute this to a combination of distributed, sub‐seismic, ‘cryptic’ strain, likely related to de‐watering, grain‐scale deformation and related changes in bulk sediment volume. This work has implications for assessing MTCs strain distribution and provides a practical approach for evaluating structural interpretations within such deposits.  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

浅述深水水道的形态学特征          下载免费PDF全文

尚文亮  徐少华  李小刚  秦磊  杨丽玉  刘家恺  姚雪婷 《地质找矿论丛》2020,35(2):204-209

目前深水水道的分类方案较多,本文基于深水水道的形态学特征,且聚焦于单一型深水水道,将其划分为顺直型(曲率介于1～1.25)、低弯度S型(曲率介于1.25～1.5)和高弯度S型(曲率1.5)。其中,顺直型水道侵蚀作用最强,往往不发育天然堤沉积,无侧向加积;低弯度S型水道发育天然堤,并具有侧向加积;高弯度S型天然堤及侧向加积最为发育,决口扇常与之伴生。深水水道的曲率是水道形态的直观表现,曲率大小主要受深水地貌即深水地形坡度的影响。在上陆坡区域,地形坡度较大,沉积物能量强,深水水道以顺直型为主。中陆坡区域,随着地形坡度的减缓,水道的弯曲形态也逐渐增加,形成低弯度S型,直至下陆坡,水道演变为高弯度的S型。  相似文献   

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer   总被引：1，自引：1，他引：0  

Basant Yadav  Shashi Mathur  Sudheer Ch  Brijesh Kumar Yadav 《水文科学杂志》2018,63(2):210-226

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.  相似文献   

Modelling the effect of changing precipitation inputs on deep soil water utilization          下载免费PDF全文

Ji Qi  Daniel Markewitz  David Radcliffe 《水文研究》2018,32(5):672-686

Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency.  相似文献   

Macrobenthic infaunal communities associated with deep‐sea hydrocarbon seeps in the northern Gulf of Mexico          下载免费PDF全文

Travis W. Washburn  Amanda W. J. Demopoulos  Paul A. Montagna 《Marine Ecology》2018,39(3)

There are thousands of seeps in the deep ocean worldwide; however, many questions remain about their contributions to global biodiversity and the surrounding deep‐sea environment. In addition to being globally distributed, seeps provide several benefits to humans such as unique habitats, organisms with novel genes, and carbon regulation. The purpose of this study is to determine whether there are unique seep macrobenthic assemblages, by comparing seep and nonseep environments, different seep habitats, and seeps at different depths and locations. Infaunal community composition, diversity, and abundance were examined between seep and nonseep background environments and among three seep habitats (i.e., microbial mats, tubeworms, and soft‐bottom seeps). Abundances were higher at seep sites compared to background areas. Abundance and diversity also differed among microbial mat, tubeworm, and soft‐bottom seep habitats. Although seeps contained different macrobenthic assemblages than nonseep areas, infaunal communities were also generally unique for each seep. Variability was 75% greater within communities near seeps compared to communities in background areas. Thus, high variability in community structure characterized seep communities rather than specific taxa. The lack of similarity among seep sites supports the idea that there are no specific infauna that can be used as indicators of seepage throughout the northern Gulf of Mexico, at least at higher taxonomic levels.  相似文献