TM图像线性体信息微机自动提取

本文采用图像平滑、边缘跟踪、霍夫变换、逆霍夫变换等综合图像处理的技术,从卫星TM图像中提取线性体信息。在霍夫变换后的累加器阵列中采用局部最大值选择,逆霍夫变换后采用剖面分析等人-机交互处理方法,结合遥感地质学家的图像解泽经验,从而提高了线性体信息自动提取的精度。     

Remote sensing techniques to locate ground water potential zones in upper Urmil River Basin, district Chhatarpur — Central India

In the present study efforts have been made to locate favourable zones for ground water targeting using IRS-USS-II data, Hydromorphogeological lineament, lineament density and ground water prospect maps have been prepared through visual interpretation of geocoded images on 1:50,000 scale and Survey of India topographical maps of the same scale. The resulting base line information has been integrated for evaluating ground water potential of mapping units. The alluvial plain, flood plain, infilled valley and deeply buried pediplain ace the prospective zones of ground water exploration and development. Fractures and faults parallel to drainage courses constitute priority zones for ground water targetting.     

Delineation and assessment of Central Indian Suture through lineament extraction approach using Remote Sensing and Geographic Information System

Lineament extraction approach allowed mapping of larger number of lineaments in a classical manner in tectonic and structural studies. In the present study, various techniques were adopted to extract lineaments using Landsat ETM+ images. To remove the biasness of the images, some pre-processing techniques like stream ordering, band differencing, colour texturing were employed to enhance the edges of the structural features. Extracted lineaments and its distribution and orientation were mapped using ArcGIS Spatial analyst tool. Results of the study showed maximum number of lineaments were oriented in the ENE–SWS direction with 63° overall inclination. Accuracy assessment and validation were made with respect to visual interpretation and overlaying the lineament on Digital Topographic Model as well as with respect to an existing geological lineament map of the study site. The result of accuracy assessment indicates higher compatibility of Central Indian Suture with the geological map of the study area.     

Extraction of morphotectonic features from DEMs: Development and applications for study areas in Hungary and NW Greece

A procedure for the consistent application of digital terrain analysis methods to identify tectonic phenomena from geomorphology is developed and presented through two case studies. Based on the study of landforms related to faults, geomorphological characteristics are translated into mathematical and numerical algorithms. Topographic features represented by digital elevation models of the test areas were extracted, described and interpreted in terms of structural geology and geomorphology. Digital terrain modelling was carried out by means of the combined use of: (1) numerical differential geometry methods, (2) digital drainage network analysis, (3) digital geomorphometry, (4) digital image processing, (5) lineament extraction and analysis, (6) spatial and statistical analysis and (7) digital elevation model-specific digital methods, such as shaded relief models, digital cross-sections and 3D surface modelling. A sequential modelling scheme was developed and implemented to analyse two selected study sites, in Hungary and NW Greece on local and regional scales. Structural information from other sources, such as geological and geophysical maps, remotely sensed images and field observations were analysed with geographic information system techniques. Digital terrain analysis methods applied in the proposed way in this study could extract morphotectonic features from DEMs along known faults and they contributed to the tectonic interpretation of the study areas.     

Fortran 77 programs for preliminary summarisation of lineament patterns

Fortran 77 programs have been developed for summarisation of lineament pattern and fracture traces on MS-DOS based micro-computer system. Two menu driven programmes CLAFY and HIST perform the above task. CLAFY reads the X, Y co-ordinates of lineament and points from an input file and produces an output file containing azimuth frequency, total length and average length for 18 predefined azimuth classes at 10 degree interval. HIST reads the output generated by CLAFY and offers an option to generate histograms of azimuth frequency/total length/average length on a dot matric printer. CLAFY and HIST were tested using digitised lineament co-ordinates derived from lineament maps of parts of Central Kerala region, southwestern, India which were prepared based on Landsat imagery interpretation. The programmes afford repetitive manipulation of large quantities of lineament data without ignoring short and /or faint lineaments which have geologic significance.     

遥感图像线性构造信息微机机助提取

本文采用图像平滑、边缘跟踪、霍夫变换、逆霍夫变换等综合图像处理技术，从卫星ＴＭ图像中提取线性体信息。在霍夫变换后的累加器阵列中采用局部最大值选择，逆霍夫变换后采用剖面分析等人一机交互处理方法，结合遥感地质学家的图像判读经验，提高了线性体信息自动提取的精度。     

香花岭地区多源地学信息复合处理图像的地质应用

本文介绍了遥感、物化探、地质等信息复合图像增强处理的基本技术方法.对多源地学信息复合处理图像新发现的色异常、线性和环形构造的分布、影像特征做了较详细的叙述;并探讨了该区的环形和线性构造对多金属矿的控矿作用,认为香花岭地区是环形和线形构造的复合控矿,在此基础上建立了香花岭矿田遥感影像模式:在环形构造内,发育北西向和北东向线性构造组合,交叉成“井”字型格式,控制金属矿的分布.根据已知矿床特征和相应影像特征的关系,提出细密线性影像和环、线交切影像两种找矿标志,指出上述标志在湘南花岗岩发育地区具有普遍找矿意义.文章最后根据多种信息综合分析,圈定了两个找矿有望地段,取得了良好的找矿效果.     

Regional mapping of the South Armorican shear zone (Brittany, France) using remotely sensed SPOT imagery

The South Armorican shear zone is one of the major occurrences of Variscan basement in Europe. The objectives of the study were to compile a lineament and a structural map from remotely sensed SPOT imagery. Transparent color films, enhanced by high-pass filtering and by stretching color composites, and spatial filtered images were analysed. The major structure mapped corresponds to a dextral transcurrent shear belt in which C and S axes have been described. Late Paleozoic and Tertiary fracturations truncate these structures. The localisation, extension and geometry of granite domes were also precised. Lastly, this study provides a good example of structural interpretation of a moderate-relief terrain using high-resolution SPOT imagery.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

A comparative evaluation of IRS-1A LISS-II and landsat MSS images using computer-aided lineament data analysis

Lineament patterns detected from remotely sensed data provide useful information to geoscientists, specially in the study of basement tectonics, groundwater targetting and mineral exploration. Improvements in the spatial resolution of satellite images have resulted in the detection of short and faint lineaments which have hitherto gone unnoticed The IRS-1A LISS-II data offers a significant improvement in spatial resolution as compared to the Landsat MSS. A set of computer programmes developed for analysis of lineaments were used to study the parameters such as lineament frequency, length and density in order to quantify the added information derived using IRS-1A LISS-II images. The incremental contribution of LISS-II images are of the order of 100 per cent for lineament frequency and about 60 per cent for total line kilometers of lineaments detected.     

MAPPING THE SOILS OF THE MAJOR NATURAL ZONES OF THE USSR FROM REMOTE SENSING IMAGERY

The authors describe the results of their experience in the use of remote sensing imagery to map the distribution of soils within the major natural zones (e.g., wooded steppe, taiga, semidesert) of the USSR. Considerable attention is devoted to instrumental-visual methods of interpretation before the focus shifts to automated interpretation and methods of map compilation, particularly general mapping at intermediate and large scales. One section assesses the relative sizes of mesorelief forms and fields in various natural zones in an effort to determine at what image scales the soil types indicated by these features will be identifiable. Translated by Edward Torrey, Alexandria, VA 22308 from: G. V. Dobrovol'skiy and V. L. Andronikov, eds., Aerokosmicheskiye metody v pochvovedenii i ikh ispol'zovaniye v sel'skom khozyaystve: sbornik nauchnykh trudov [Remote Sensing Methods in Soil Science and Their Utilization in Agriculture: A Collection of Scientific Works]. Moscow: Nauka, 1990, pp. 22-34.     

Remote Sensing of the Lineament Patterns Associated with the Great Boundary Fault of Rajasthan

The present work aims to identify, isolate, map and analyse the lineament patterns associated with the Great Boundary Fault (GBF) using remote sensing techniques and to draw some significant conclusions regarding its tectonogenesis therefrom. The locus of the line separating the two distinct patterns of structural trend lines observed on a structural trend map, prepared from the Landsat images, represents the ground disposition of the GBF. The rose diagram plotted for the lineament orientations of different sectors indicates that the whole area was subjected to a similar stress pattern, yet the intensity of stress in each sector was significantly different from the other. The lineament intersection density map clearly demarcates the areas of folding and faulting associated with the GBF. Tectonic anomalies plotted for the area through lineament analysis represent the signatures of prominent surface and sub-surface tectonic elements. The work presents an example for the study of a Precambrian fault system using remote sensing techniques.     

AUTO-IDENTIFICATION OF FOREST FIRE-POINTS IN NOAA IMAGES BASED ON NEURAL NETWORK

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Auto-identification of forest fire-points in NOAA images based on neural network

Identification of forest fire-points in NOAA images in the basis of monitoring forest fire using NOAA satellite data. Traditional visual interpretation is difficult to settle for auto identification with computer. The artificial neural network technique provides a new means for solving this problem. In this paper, the principles and method of using neural network technique to automatically identify fire-points in NOAA images are discussed and the test in the range of Hubei province is presented. The result of the test shows that the disciplined neural network has collected the character of fire-points and has ability to identify fire-points in NOAA images. Comparing neural network with visual interpretation, the conclusion is drawn that by using neural network the purpose of auto-identification of forest fire-points in NOAA images can be realized with the almost same precision.     

多尺度全卷积神经网络建筑物提取

针对VGG16网络在高空间分辨率遥感影像中进行大型建筑物提取时存在空洞的现象,提出一种基于多尺度影像的建筑物提取方法。将原始影像进行不同尺度的下采样,提取不同尺度下的建筑物特征,并将这些多尺度特征相加合并,同时为了减少网络参数数量,用全卷积上采样过程代替原始VGG16网络中的全连接层进行建筑物提取。以0.5 m分辨率的上海市嘉定区影像和1 m分辨率的Massachusetts地区影像进行试验,精度分别达97.09%和96.66%,表明本文方法的有效性。     

Ranchi mega lineament and its correlation with geological and geophysical data

Remotely-sensed satellite data of Landsat 5 (TM) and IRS-1A (LISS II) covering parts of central portion of Chotanagapur plateau have been analysed visually to delineate some prominent lineaments in Ranchi district and its adjoining area. One of the most prominent lineament “Ranchi mega lineament? has been analysed in this paper. This lineament is a potential site for base metal mineralisation at some places, and it acts as good groundwater conduit in the region. No earlier attempt to map this entire structural unit as a separate entity is known. Geophysical bouguer gravity anomaly map, lithological and structural maps have some remarkable correlation with this lineament. In the present paper authors have tried to analyse the lineament with respect to its tectonic, mineralisation and groundwater prospects. Resistivity survey carried in close proximity of this lineament at selected sites, indicate that rocks are well fractured at depth. Available lithological and structural map support this lineament as a shear near Purulia (W.B.) and also having base-metal mineralisation potential in this area. Authors opine that existing gravity anomaly map may be further augmented in the light of present study.     

STUDY OF THE MORPHOSTRUCTURES OF MOUNTAINOUS AREAS FROM SPACE IMAGERY

The authors review recent developments in the USSR in the use of remote sensing imagery in the analysis, at the world-regional and local levels, of major relief elements (and structural elements manifest in relief) typical of mountainous country. Keys to their interpretation at a variety of scales are described. Case studies and detailed interpretation of imagery in the Soviet Far East and Central Asia (large-scale) and the Caucasus (intermediate-scale) provide for the identification of all morphostructural elements possible through traditional analysis, and in the latter case, for the first direct detection of transverse (cross-cutting) faults. Translated from: Geomorfologiya, 1988, No. 1, pp. 16-29.     

COLOR SYNTHESIS OF MULTISPECTRAL IMAGERY FOR MAPPING PURPOSES

The authors describe common Soviet practice in the making of false-color composite imagery for visual interpretation. Also addressed are standard image interpretation techniques: figure-ground color contrast, color separation (number of color gradations occurring within an average contrast range), and so-called landscape indicator techniques. Related issues, such as selection of spectral bands and filters for specific interpretation tasks, and the changing reflectance characteristics of band-specific intermediate images, receive attention as well. Tables and figures illustrating relationships between photographic contrast, film contrast ratings, optical densities, and spectral bands are included. Translated from: Geodeziya i kartografiya, 1985, No. 4, pp. 41-46.     

Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data

A set of techniques was developed for automatically detecting tectonic lineaments from multi-source remotely-sensed data at various scales. The techniques include adaptive shading of grid data to enhance linear features, a segment-tracing algorithm to extract line segments from the shaded grid data, grouping of the segments by concatenating short segments, and connecting them by proximity and co-linearity criteria to form a lineament that represents significant tectonic structure. B-spline smoothing was adopted for lineament representation. Finally, a technique for assessing the orientations and styles of faulting (normal, reverse, and strike-slip types) was developed for use in characterizing the extrapolated fracture planes. The applicability of the developed techniques was examined using 30 arc-second topography/bathymetry grids, 1-min gravity anomaly grids, and 2-min total field magnetic intensity grids covering Egypt and its surroundings. Lineaments derived from data types so diverse in composition and from various depths corresponded well with the referenced tectonic features over much of the region. Prominent trends and faulting styles of lineaments provided important clues as to the timing of their development as well as strong support for a structural inheritance model. Results demonstrated the effectiveness of the developed techniques combined with integration of remotely-sensed data in detecting regional fracture systems accurately and in characterizing geodynamics over a long timeframe.