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801.
以美国地球资源卫星的149轨道第32景1994年8月2日7个波段的数据,使用通用软件对图像资料进行色阶调整和通道合成,以优选的配色方案合成新疆阿图什市以北的哈拉峻-皮羌地区的彩色卫星影像.尝试对研究区各种地貌、地质构造与地层的影像特征进行初步分析,对活动构造的影像特征做出解释. 相似文献
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Optical image classification converts spectral data into thematic information from the spectral signature of each object in the image. However, spectral separability is influenced by intrinsic characteristics of the targets, as well as the characteristics of the images used. The classification process will present more reliable results when aspects associated with natural environments (climate, soil, relief, water, etc.) and anthropic environments (roads, constructions, urban area) begin to be considered, as they determine and guide land use and land cover (LULC). The objectives of this study are to evaluate the integration of environmental variables with spectral variables and the performance of the Random Forest algorithm in the classification of Landsat-8 OLI images, of a watershed in the Eastern Amazon, Brazil. The classification process used 96 predictive variables, involving spectral, geological, pedological, climatic and topographic data and Euclidean distances. The selection of variables to construct the predictive models was divided into two approaches: (i) data set containing only spectral variables, and (ii) set of environmental variables added to the spectral data. The variables were selected through nonlinear correlation analysis, with the Randomized Dependence Coefficient and the Recursive Feature Elimination (RFE) method, using the Random Forest classifier algorithm. The spectral variables NDVI, bands 2, 4, 5, 6 and 7 of the dry season and band 4 of the rainy season were selected in both approaches (i and ii). The Euclidean distance from the urban area, Arenosol soil class, annual precipitation, precipitation in February and precipitation of the wettest quarter were the variables selected from the auxiliary data set. This study showed that the addition of environmental data to the spectral data reduces the limitation of the latter, regarding the discrimination of the different classes of LULC, in addition to improving the accuracy of the classification. The addition of soil classes to spectral variables provided a reduction in errors for vegetation classification (Evergreen Forest and Cerrado Sensu Stricto), as it was able to inform about nutrient availability and water storage capacity. The study demonstrates that the addition of environmental variables to the spectral variables can be an alternative to improve monitoring in areas of ecotone in Neotropical regions. 相似文献
804.
提出了一种有效的道路现状普查模式,利用数学形态学和矢量化相结合的方法,根据最新遥感影像对大面积区域路网进行适时提取。能够在较短的时间内将大面积区域的现状路网数据入库并制作专题路网现状地图,通过实际应用,证明方法可靠,可推广利用。 相似文献
805.
Xiaojun Yang Liding Chen 《International Journal of Applied Earth Observation and Geoinformation》2010
Landslides are a major type of geohazards claiming thousands of casualties and billions of dollars in property damages every year. Catastrophic landslide activities are often triggered by some extreme events such as earthquakes, excessive precipitations, or volcanic eruptions. Quickly identifying the spatial distribution of landslides induced by these extreme events is crucial for coordinating rescue efforts and planning in situ investigations. In this study, we propose an automated method for detecting the spatial distribution of earthquake-triggered landslides by examining after-event vegetation changes. Central to this method is the use of pre- and post-event remote sensor images covering the same area. Geometric correction and radiometric normalization are performed before deriving a vegetation index from each image. Then, an image differencing procedure is applied to the two derived indices. With the resultant difference image, an initial landslide distribution map is generated by highlighting the pixels with a threshold percentage decrease in the brightness values as a direct result of the image subtraction. The threshold percentage value is interactively determined by using a visual interpretation method. The final landslide distribution map is produced after using a modal filter to suppress boundary errors in the initial map. This method has been implemented in a test site, approximately 30 km from the epicenter of the Sichuan earthquake (7.9 Ms) that struck on 12 May 2008. A pre-event Thematic Mapper image and a post-event Advanced Spaceborne Thermal Emission and Reflection Radiometer scene are used. The thematic accuracy assessment indicates that 90% of the landslides have correctly been mapped. Given the relatively simple procedures and the good mapping accuracy, the image processing and change detection method identified in this study seems to be promising from an operational perspective. 相似文献
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The application of nautical X-Band radars to measure internal wave (IW) properties is investigated. A methodology based on the use of Radon transform (RT) techniques to detect internal wave related features from backscatter image sequences is introduced to compute properties such as direction of propagation, non-linear velocity (c0), distance between solitons (Lcc) and number of solitons per packet. The proposed methodology was applied to several events recorded by a ship-mounted X-Band radar system (WaMoS) during the NLIWI experiment in 2006. Results from the comparisons to simultaneous measurements taken at neighboring oceanographic moorings indicated that c0 can be estimated with a RMS error of 0.06 m s−1, which corresponds to a mean relative error of −1.4%. Similarly, Lcc can be estimated with a RMS error of 98 m, which is associated with a mean relative error of 14.6%. This latter error estimate however is likely to be overestimated, because it reflects strongly the separation between sampling stations as Lcc was shown to be highly dependent on propagation distance. The accuracy of the results shows that X-Band systems are well suited to measure internal wave properties offering some advantages over SAR and other in situ devices. 相似文献
810.
Experiments on vision guided docking of an autonomous underwater vehicle using one camera 总被引:1,自引:0,他引:1
This paper introduces an underwater docking procedure for the test-bed autonomous underwater vehicle (AUV) platform called ISiMI using one charge-coupled device (CCD) camera. The AUV is optically guided by lights mounted around the entrance of a docking station and a vision system consisting of a CCD camera and a frame grabber in the AUV. This paper presents an image processing procedure to identify the dock by discriminating between light images, and proposes a final approach algorithm based on the vision guidance. A signal processing technique to remove noise on the defused grabbed light images is introduced, and a two-stage final approach for stable docking at the terminal instant is suggested. A vision-guidance controller was designed with conventional PID controllers for the vertical plane and the horizontal plane. Experiments were conducted to demonstrate the effectiveness of the vision-guided docking system of the AUV. 相似文献