基于遥感图像增强的海岸线提取方法

在人类活动和自然环境的影响下,海岸带长期处于变化状态,准确提取和实时监测海岸线变化对我国海岸带的利用与开发具有重要意义。文章利用Landsat TM遥感图像和ERDAS IMAGINE遥感图像处理软件的监督分类和图像增强方法,提出两种海岸线获取方法,完成海岸线的自动提取,实现海岸带的大面积同步和动态监测。     

Deep learning based classification of rock structure of tunnel face

The automated interpretation of rock structure can improve the efficiency,accuracy,and consistency of the geological risk assessment of tunnel face.Because of the high uncertainties in the geological images as a result of different regional rock types,as well as in-situ conditions(e.g.,temperature,humidity,and construction procedure),previous automated methods have limited performance in classification of rock structure of tunnel face during construction.This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks(CNN),namely Inception-ResNet-V2(IRV2).A prototype recognition system is implemented to classify 5 types of rock structures including mosaic,granular,layered,block,and fragmentation structures.The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images.Furthermore,different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter.Among all the discussed models,i.e.,ResNet-50,ResNet-101,and Inception-v4,Inception-ResNet-V2 exhibits the best performance in terms of various indicators,such as precision,recall,F-score,and testing time per image.Meanwhile,the model trained by a large database can obtain the object features more comprehensively,leading to higher accuracy.Compared with the original image classification method,the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence.The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.     

小波变换在遥感图像压缩中的应用及Matlab实现

遥感图像在地理信息系统中的应用越来越广，但遥感图像所含的数据量较大。为了方便快速地实现遥感图像的浏览，必须对图像进行压缩。可基于离散小波变换的二维小波分析，采用对遥感图像进行压缩并结合MATLAB来实现。     

Qualitative and Quantitative Characterization of Shale Microscopic Pore Characteristics Based on Image Analysis Technology

In order to evaluate the shale microscopic pore characteristics more economically and effectively in limited circumstances, the pore characteristics of Longmaxi Formation in Pengshui area, Sichuan Basin, were qualitatively observed and analyzed with Scanning Electron Microscopy (SEM) and Field Emission Scanning Electron Microscopy (FESEM) with argon ion polishing method at the same time. Pore quantitative information were extracted from shale SEM and FESEM images with the help of a professional image analysis software IamgeJ2x, and combined with statistical methods, the whole pore size distribution as well as shale pore fractal dimension and the relevance between fractal dimension and organic matter content, mineral composition and pore adsorption capacity and the corresponding pore structure characteristics of Longmaxi Formation in Pengshui area were analyzed. The study shows that under SEM, there are mostly micro pores of Longmaxi Formation in Pengshui area. The main pore types include intergranular pore, clay mineral layer pore, intragranular pore and micro cracks, etc. Through argon ion polishing FESEM, there mainly develop nanoscale pores. The main pore types contain organic pore, inorganic mineral pore (pyrite intergranular pore, intragranular pore, clay mineral layer pore and intergranular pore, etc.) and micro cracks. The use of both of the two methords is more advantageous to qualitatively analyze shale pore. The whole pore size distribution of shale pore has four main peaks and the main distribution range is 3~10 nm, 10~40 nm, 100~400 nm, 1~4 μm, respectively. The shape factor of shale organic matter pore is distributed between 0.9~1 and inorganic mineral pore is distributed between 0.5~0.7. It shows that the organic matter pore is circular, nearly circular and inorganic mineral pore shape is triangle, polygon, slit shape and so on. The inorganic mineral pore shape is relativly complex because of the different pore causes. The shale pore of Longmaxi Formation in Pengshui area conforms to the fractal features, and the organic pore fractal dimension is smaller than that of inorganic mineral pore, showing that the organic matter pore structure is relatively simple. There is a certain relevance between fractal dimension and organic matter content, mineral composition, porosity, and adsorbed gas content. With the increase of the organic matter content, the shale pore fractal dimension increase, the pore structure characteristics become complicated. With the shale pore fractal dimension increasing, the biggest gas adsorption quantity increases and the ability of pore adsorption strengthens.     

SAR与光学影像融合在宁陕地区明长城探测中的应用研究

宁夏和陕西地区的明长城在遭到自然与人为双重破坏后日渐退化,有的区段已消失于地表。本文采用了SAR与光学影像像素 级融合方法——HIS变换融合和PC主成份变换融合,通过比较2种方法的融合效果,最终选定PC主分量变换融合的结果作为自动提取 该区明长城信息的基础。通过“骨架”算法实现了对该区明长城空间位置的自动提取,最终获得了较为清晰的明长城信息。该区明 长城准确而完整的空间位置,可为进一步研究该区明长城沿线环境变化及古环境演变提供精准的研究范围和研究基础。     

遥感图像应用处理中的一个分类新算法——模拟目视分辨法

本文介绍的SVD分类方法,模拟人眼分辨图像上图斑的机理,对遥感图像进行分类。意在克服常规计算机分类的一些缺点,改善图斑边缘及交界处的混分现象,同时做到均匀大图斑的平滑及孤立小图斑的保留,使分类图上图斑整齐,界限分明,精度高,视觉效果好。因此有较好的实际应用价值。     

A combined image matching method for Chinese optical satellite imagery

Image matching is one of the key technologies for digital Earth. This paper presents a combined image matching method for Chinese satellite images. This method includes the following four steps: (1) a modified Wallis-type filter is proposed to determine parameters adaptively while avoiding over-enhancement; (2) a mismatch detection procedure based on a global-local strategy is introduced to remove outliers generated by the Scale-invariant feature transform algorithm, and geometric orientation with bundle block adjustment is employed to compensate for the systematic errors of the position and attitude observations; (3) we design a novel similarity measure (distance, angle and the Normalized Cross-Correlation similarities, DANCC) which considers geometric similarity and textural similarity; and (4) we introduce a hierarchical matching strategy to refine the matching result level by level. Four typical image pairs acquired from Mapping Satellite-1, ZY-1 02C, ZY-3 and GeoEye-1, respectively, are used for experimental analysis. A comparison with the two current main matching algorithms for satellite imagery confirms that the proposed method is capable of producing reliable and accurate matching results on different terrains from not only Chinese satellite images, but also foreign satellite images.     

Beach disturbance caused by off-road vehicles (ORVs) on sandy shores: relationship with traffic volumes and a new method to quantify impacts using image-based data acquisition and analysis

Vehicles cause environmental damage on sandy beaches, including physical displacement and compaction of the sediment. Such physical habitat disturbance provides a relatively simple indicator of ORV-related impacts that is potentially useful in monitoring the efficacy of beach traffic management interventions; such interventions also require data on the relationship between traffic volumes and the resulting levels of impact. Here we determined how the extent of beach disturbance is linked to traffic volumes and tested the utility of image-based data acquisition to monitor beach surfaces. Experimental traffic application resulted in disturbance effects ranging from 15% of the intertidal zone being rutted after 10 vehicle passes to 85% after 100 passes. A new camera platform, specifically designed for beach surveys, was field tested and the resulting image-based data compared with traditional line-intercept methods and in situ measurements using quadrats. All techniques gave similar results in terms of quantifying the relationship between traffic intensity and beach disturbance. However, the physical, in situ measurements, using quadrats, generally produced higher (+4.68%) estimates than photos taken with the camera platform coupled with off-site image analysis. Image-based methods can be more costly, but in politically and socially sensitive monitoring applications, such as ORV use on sandy beaches, they are superior in providing unbiased and permanent records of environmental conditions in relation to anthropogenic pressures.     

环境与灾害监测预报小卫星CCD数据质量评价

中国环境与灾害监测预报小卫星星座双星4部CCD传感器具备大范围、全天时环境与灾害监测等方面的能力,在空间覆盖与重复观测频率方面有很大的优势。结合美国陆地卫星TM数据,从几何精度、辐射质量2个方面定量分析了该卫星CCD传感器的数据质量。结果表明：①环境减灾卫星CCD数据无波段错位现象,有极小的畸变与扭曲（0.05%）,然...     

A new method to identify void constrictions in micro-CT images of sand

Micro-Computed Tomography (micro-CT) provides 3D images of the internal structure of sands, allowing quantitative measurements of internal features, including void topology. Methods have already been proposed to measure constriction sizes from idealised particle arrangements or from micro-CT data, however the 3D geometry of constrictions in sands is extremely complex and can be difficult to interpret using existing methods. This paper outlines a new method to measure and visualise void constrictions in sands using micro-CT data, with a view to assessing performance of granular filters. The method is based on watershed segmentation of the void space. Synthetic data obtained from DEM simulations are used to validate the new algorithm and its performance against existing image-based methods is assessed by considering micro-CT data for a representative filter material.