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1.
Lithological Mapping in the Eastern Part of the Central Iranian Volcanic Belt Using Combined ASTER and IRS data 总被引：1，自引：0，他引：1
Seyed Mohammad Hadi Hadigheh Hojjatollah Ranjbar 《Journal of the Indian Society of Remote Sensing》2013,41(4):921-931
The study area is located in the eastern part of the central Iranian volcanic belt. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS ) pan images were used for applying several image classification methods for lithological mapping. ASTER visible-near infrared and shortwave infrared bands were sharpened using IRS pan image. We used classification methods such as Maximum likelihood, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) in order to evaluate the usefulness of these methods for geological mapping. The classification results showed that MLC has the best accuracy and the classified image closely resembles the previously prepared geology map of the area. 相似文献
2.
R. S. Negi P. F. Augustine J. K. Nanda 《Journal of the Indian Society of Remote Sensing》1988,16(3):7-12
An area of 2500 sq. Km. has been covered by photogeological mapping with selected field checks which forms a part of Cuddapah basin in the Kumool and Prakasam Districts of A.P. The rocks exposed in the area belong to the Cumbum and Bairankonda Formations of Cuddapah Super Group. They occur more or less as an alternating sequence. These formations are bound on the east by Dharawar gneisses and on the west by Nandyal Shales. The Bairankonda Formation is predominantly arenaceous with quartzite as the main member, being easily identifiable in the aerial photographs by its characteristic topography, coarse drainage, light grey tone etc. The Cumbum Formation is mainly argillaceous with slate/phyllite as the main member, intercalated with minor quartzite band and is expressed on the photographs in dark tone with dendritic to sub-parellel drainage and occurs relatively at lower elevations, mostly in plains. The Dharwar (Archaean) gneisses underlying the vast plains in the east are recognised by the sandy nature of soil and by the thrusted contact of this unit with the Bairankondas. The Nandyal Shales occupying the large cultivated area in west have a darker tone and a dendritic drainage pattern. They are Upper Kurnool in age. All the litho-units have undergone pre-Kurnool deformation resulting into plunging/doubly plunging antiforms and synforms trending NNE-SSW and a few shear zones and faults. Due to severe deformation a very prominant foliation has developed in the Cumbum unit. Groove lineation has also been developed in the hinge portions of major folds. The magmatic activity in these formations has been witnessed with the emplacement of reibeckite syenite and Kimberlite plugs and veins. Geomorphologically the area may be divided into two prominant units namely (i) Denudational landforms comprising (a) hills, ridges, inselbergs, bornhardts and hogbacks on Bairankonda quartzites and phyllites (b) pediment and dissected pediment on Cumbum Shales and Dharwar gneisses and (ii) Depositional landforms such as (a) alluvial fans and colluvial fans (b) alluvial valley fills along river courses and (c) sand dunes in Dharwar gneisses. 相似文献
3.
Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics. 相似文献
4.
In this study, we proposed an automated lithological mapping approach by using spectral enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible Infrared Imaging Spectroradiometer-Next Generation (AVIRIS-NG) hyperspectral data in the greenstone belt of the Hutti area, India. We integrated spectral enhancement techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformation and different MLAs for an accurate mapping of rock types. A conjugate utilization of conventional geological map and spectral enhancement products derived from ASTER data were used for the preparation of a high-resolution reference lithology map. Feature selection and extraction methods were applied on the AVIRIS-NG data to derive different input dataset such as (a) all spectral bands, (b) shortwave infrared bands, (c) Joint Mutual Information Maximization (JMIM) based optimum bands, and (d) optimum bands using PCA, to choose optimum input dataset for automated lithological mapping. The comparative analysis of different MLAs shows that the Support Vector Machine (SVM) outperforms other Machine Learning (ML) models. The SVM achieved an Overall Accuracy (OA) and Kappa Coefficient (k) of 85.48% and 0.83, respectively, using JMIM based optimum bands. The JMIM based optimum bands were more suitable than other input datasets to classify most of the lithological units (i.e. metabasalt, amphibolite, granite, acidic intrusive and migmatite) within the study area . The sensitivity analysis performed in this study illustrates that the SVM is less sensitive to the number of samples and mislabeling in the model training than other MLAs. The obtained high-resolution classified map with accurate litho-contacts of amphibolite, metabasalt, and granite can be coupled with an alteration map of the area for targeting the potential zone of gold mineralization. 相似文献
5.
Inderjit Singh 《Journal of the Indian Society of Remote Sensing》1988,16(4):43-52
LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.) 相似文献
6.
Digital image processing on IRS-1C-LISS-III data acquired on October 13, 1998 has been carried out to map the land use classes
in part of the Kandi belt, the submontane tract lying in the Outer Himalaya of Jammu region of Jammu and Kashmir. Supervised
classification has been combined with rule-based classification to delineate various land use classes. The various categories
of land use in the area recognized are forest, agriculture, riverbed, urban, fallow, wasteland and water. Forest is dominant
along the upper boundary of the Kandi belt (along Siwalik) and on ridges, whereas, agriculture land is mainly along the lower
boundary (along Sirowal) of the study area. 相似文献
7.
P K Champatiray A K Roy B Prabhakaran 《Journal of the Indian Society of Remote Sensing》1995,23(2):77-86
The C-band imaging radar of ERS-1, due to its high sensitivity to terrain surface features, holds tremendous potential in topographic terrain mapping for various applications. This is being examined for geological applications, mainly structural and lithological mapping in a mineral belt of Bihar and Orissa, India. The high image contrast that facilitates structural interpretation and highlights topography on the SAR images, reflects the high sensitivity of the ERS-1-SAR to change in terrain slope in the study area. Extensive lineaments, fold structure and major lithological contacts are easily mappable from the SAR imagery. Many of the lineaments, lithological contacts and fold pattern are mapped equally from optical data (Landsat-TM and IRS-1B FCC). The close association of fold pattern and mineral deposits in the region has necessitated the study of those structures carefully from various remote sensing data products. Synergism between SAR and TM provided useful results regarding structure and lithology of the region. The advantage of SAR in highlighting topography and detecting lineaments are affected to a great extent by the speckle noise and low pixel resolution. The present study shows that future geologic interpretation demands high spatial resolution and efficient data processing technique which reduces the speckle noise more significantly. 相似文献
8.
Karbi Anglong and North Cachar Hills districts of Assam are endowed with rich and diverse vegetation resources. Increased human pressure due to shifting cultivation and raw material extraction for industrial purposes are heavily altering the forested landscape. The present study deals with mapping of forest types in the two districts using LANDSAT-MSS digital data. The maps thus generated provide spatial distribution of bioclimatic vegetation types. Supervised maximum likelihood classification has been performed using training sets collected during field work. The spectral behaviour of vegetation types have been studied for optimising classification scheme. The classification accuracy of classes mapped has been calculated. 相似文献
9.
《International Journal of Digital Earth》2013,6(2):143-157
Abstract This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron (MLP) in the Zagros Folded Belt, Iran, and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact assessment. MLP neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data, and the results were compared using confusion matrices. The most appropriate classification image for L1B input to MLP was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%. The MLP result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs, respectively. Field observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified remotely. It is concluded that MLP is an efficient approach for mapping salt diapirs and salt-affected areas. 相似文献
10.
Bendix 11 channel aerial MSS data have been used to generate landuse thematic classification using Image‐100 interactive image processing system for a part of Kamiichi area, Japan. Two different classification schemes were attempted, once with Visible and IR data using supervised classification technique and then with Thermal IR data using density slicing technique. Comparison of both the results shows that main features of interest are uniquely classified in both the cases. In addition, higher order classification could also be achieved using Visible, IR and Thermal IR data for each class of interest. 相似文献
11.
Gayantha R. L. Kodikara Tsehaie Woldai 《International Journal of Digital Earth》2018,11(10):1020-1038
Here, we demonstrate the application of Decision Tree Classification (DTC) method for lithological mapping from multi-spectral satellite imagery. The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya. The work involves the collection of rock and soil samples in the field, their analyses using reflectance and emittance spectroscopy, and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method. The latter method is strictly non-parametric, flexible and simple which does not require assumptions regarding the distributions of the input data. It has been successfully used in a wide range of classification problems. The DTC method successfully mapped the chert and trachyte series rocks, including clay minerals and evaporites of the area with higher overall accuracy (86%). Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data. Moreover, the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately, which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction. 相似文献
12.
Ahmed Youssef 《地球空间信息科学学报》2013,16(3):182-190
In the current study, the shuttle radar topography mission (SRTM) data, with ～90 m horizontal resolution, were used to delineate the paleodrainage system and their mega basin extent in the East Sahara area. One mega-drainage basin has been detected, covering an area of 256 000 km2. It is classified into two sub mega basins. The Uweinate sub mega basin, which is composed of four main tributaries, collected water from a vast catchment region and drained eastward from the north, west, and southwest, starting at highland areas. The first subwatershed basin is in the northern plateau, south of the Abu-Balas area, with a total catchment area of 25 045 km2. The second subwatershed is in the Gilf Kebir plateau and has a total catchment area of 38 257 km2. The third subwatershed drains from the Uweinate highlands and has a catchment area of 46 154 km2. The fourth subwatershed, which is known in literature as Wadi Mokhtafi in its upper reach and Wadi Arid in its lower reach, drains the northwestern highlands of Sudan and has a total catchment area of 28 653 km2. The Tushka sub mega basin includes one watershed that drains from the northeast highlands of Sudan and has a total catchment area of 63 019 km2. The Uweinate and Tushka sub mega basins are joined together to the North of the Tushka depression, which drains northward toward the Kharga depression. This study indicates that the Eastern Sahara Mega Basin is a closed hydrological system independent of the other drainage systems, such as the Nile hydrosystem and the Qena Valley system. The present research illustrates the capability of the SRTM data in mapping the paleochannel networks, as well as estimate the catchment area and direction of the water flow. Finally, the study reveals that the four areas could be potentially used for different reclamation activities due to the ground water accumulations possibilities. 相似文献
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14.
The purpose of this study was to assess the environmental impacts of forest fires on part of the Mediterranean basin. The study area is on the Kassandra peninsula, prefecture of Halkidiki, Greece. A maximum likelihood supervised classification was applied to a post-fire Landsat TM image for mapping the exact burned area. Land-cover types that had been affected by fire were identified with the aid of a CORINE land-cover type layer. Results showed an overall classification accuracy of 95%, and 83% of the total burned area was ‘forest areas’. A normalized difference vegetation index threshold technique was applied to a post-fire Quickbird image which had been recorded six years after the fire event to assess the vegetation recovery and to identify the vegetation species that were dominant in burned areas. Four classes were identified: ‘bare soil’, ‘sparse shrubs’, ‘dense shrubs’ and ‘tree and shrub communities’. Results showed that ‘shrublands’ is the main vegetation type which has prevailed (65%) and that vegetation recovery is homogeneous in burned areas. 相似文献
15.
Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas. 相似文献
16.
The spectral angle mapper (SAM), as a spectral matching method, has been widely used in lithological type identification and mapping using hyperspectral data. The SAM quantifies the spectral similarity between an image pixel spectrum and a reference spectrum with known components. In most existing studies a mean reflectance spectrum has been used as the reference spectrum for a specific lithological class. However, this conventional use of SAM does not take into account the spectral variability, which is an inherent property of many rocks and is further magnified in remote sensing data acquisition process. In this study, two methods of determining reference spectra used in SAM are proposed for the improved lithological mapping. In first method the mean of spectral derivatives was combined with the mean of original spectra, i.e., the mean spectrum and the mean spectral derivative were jointly used in SAM classification, to improve the class separability. The second method is the use of multiple reference spectra in SAM to accommodate the spectral variability. The proposed methods were evaluated in lithological mapping using EO-1 Hyperion hyperspectral data of two arid areas. The spectral variability and separability of the rock types under investigation were also examined and compared using spectral data alone and using both spectral data and first derivatives. The experimental results indicated that spectral variability significantly affected the identification of lithological classes with the conventional SAM method using a mean reference spectrum. The proposed methods achieved significant improvement in the accuracy of lithological mapping, outperforming the conventional use of SAM with a mean spectrum as the reference spectrum, and the matching filtering, a widely used spectral mapping method. 相似文献
17.
水稻播种面积是农业管理部门关心的重要问题之一。本文介绍了证据理论在遥感图像分类上的应用,并以汉川市为示范区,采用2002年TM遥感影像,在GIS技术支持下,通过建模运算,剔除不可能是水稻的像元。然后将证据理论用于影像分类,将影像初次分类结果,与参考图对照,将不满足要求的区域提取出来,再次进行第二次分类。将第二次分类结果与参考图对照,显示分类效果满足要求;若不满足要求,可继续进行再次分类,直到分类效果满意为止。将两次分类结果中的水稻信息进行叠加,提取出水稻遥感信息,经检验精度达到91.39%。 相似文献
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19.
The objective of the study was to carry out an automatic classification of the lithological units of interest using the integration of remote sensing image, in which various objects are spread on, and terrestrial spectral measurement data. Only endmembers of interest are classified using spectral classification methods such as Spectral Angle Mapper. Following the identification of the types of rock and minerals, integration of remote sensing images and spectral measurement data enable spectral classification. In this study, Short Wave Infrared detector images of Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite and spectroradiometer measurements were used. The study area, Gölova with its geological diversity is located in the Kelkit Valley section of the North Anatolian Fault Zone in Northeast of Turkey. Seventeen rock samples were collected and their coordinates were recorded. The samples were categorized via spectral measurements on their thin sections through petrographic analyses. Marble and Meta lava with different lithological were selected as endmembers. SAM was used as the classification method that enables the analysis of the endmember with the threshold value of 0.009 radian for marble and 0.010 radian for metalava. SAM analysis was compared by visual analysis to principle component analysis, decorrelation stretch, band ratio (R: 4/7, G: 4/1, B (2/3) x (4/3)) and band combination analysis (R: 9, G: 4 and B: 5). This study demonstrates that the SAM method can be successfully used in both the integration of remote sensing image and terrestrial spectral measurement data in lithological classification. Both the endmembers of metalava and marbles were detected in the SAM results at the GPS coordinates noted down whilst collecting the rock samples for accuracy assessment. 相似文献
20.
基于ASTER遥感影像的西昆仑岩性信息提取方法研究 总被引：1，自引：0，他引：1
基于西昆仑西段布伦口地区各岩性段内岩石样品的矿物组成及其光谱特征分析,提取代表各岩性单元的岩性端元波谱曲线;对研究区内ASTER可见光(VNIR)和短波红外(SWIR)数据进行匹配滤波处理,成功提取了研究区内9种重要的岩性单元(包括古元古界布仑阔勒群的黑云石英岩、黑云斜长片麻岩、黑云石英片岩和黑云角闪斜长片麻岩,志留系温泉沟群的绿泥石绢云母板岩、黑色千枚岩和绢云母石英片岩,以及石英闪长岩和英云闪长岩)。经已知地质资料和野外查证资料分析证明,用上述方法提取岩性信息的结果可靠,能为岩性填图及矿床勘查工作提供参考。 相似文献