scholarly journals Automatic extraction of faults and fractal analysis from remote sensing data

2007 ◽  
Vol 14 (2) ◽  
pp. 131-138 ◽  
Author(s):  
R. Gloaguen ◽  
P. R. Marpu ◽  
I. Niemeyer
Keyword(s):  
Remote Sensing ◽  
Degrees Of Freedom ◽  
Power Spectra ◽  
Remote Sensing Data ◽  
Fractal Dimensions ◽  
Direct Analysis ◽  
Image Texture ◽  
Automatic Identification ◽  
Object Based ◽  
Fault Statistics

Abstract. Object-based classification is a promising technique for image classification. Unlike pixel-based methods, which only use the measured radiometric values, the object-based techniques can also use shape and context information of scene textures. These extra degrees of freedom provided by the objects allow the automatic identification of geological structures. In this article, we present an evaluation of object-based classification in the context of extraction of geological faults. Digital elevation models and radar data of an area near Lake Magadi (Kenya) have been processed. We then determine the statistics of the fault populations. The fractal dimensions of fault dimensions are similar to fractal dimensions directly measured on remote sensing images of the study area using power spectra (PSD) and variograms. These methods allow unbiased statistics of faults and help us to understand the evolution of the fault systems in extensional domains. Furthermore, the direct analysis of image texture is a good indicator of the fault statistics and allows us to classify the intensity and type of deformation. We propose that extensional fault networks can be modeled by iterative function system (IFS).

Object-based speckle filtering using multisensoral remote sensing data

2005 ◽  
Author(s):  
Björn Waske ◽  
Vanessa Heinzel ◽  
Matthias Braun ◽  
Gunter Menz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Based ◽  
Speckle Filtering

scholarly journals Object-based image analysis of remote sensing data

2011 ◽  
Vol 55 (04) ◽  
pp. 641-664 ◽  
Author(s):  
Tatjana Veljanovski ◽  
Urša Kanjir ◽  
Krištof Oštir
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS

2016 ◽  
Vol XLI-B1 ◽  
pp. 43-46
Author(s):  
Yu-Ching Lin ◽  
ChinSu Lin ◽  
Ming-Da Tsai ◽  
Chun-Lin Lin
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Surface Characteristics ◽  
Point Clouds ◽  
Unit Weight ◽  
Waveform Data ◽  
Remote Sensing Technique ◽  
Object Based

The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

scholarly journals Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

Proof of concept of a novel cloud computing approach for object-based remote sensing data analysis and classification

2018 ◽  
Vol 56 (4) ◽  
pp. 536-553 ◽  
Author(s):  
R. R. Antunes ◽  
T. Blaschke ◽  
D. Tiede ◽  
E. S. Bias ◽  
G. A. O. P. Costa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Data Analysis ◽  
Remote Sensing Data ◽  
Proof Of Concept ◽  
Sensing Data ◽  
Object Based ◽  
Computing Approach

scholarly journals AN OBJECT-BASED APPROACH FOR MONITORING THE EVOLUTION OF LANDSLIDE-DAMMED LAKES AND DETECTING TRIGGERING LANDSLIDES IN TAIWAN

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 103-108 ◽  
Author(s):  
Z. Dabiri ◽  
D. Hölbling ◽  
L. Abad ◽  
G. Prasicek ◽  
A.-L. Argentin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landsat Imagery ◽  
Contextual Information ◽  
Regional Scale ◽  
Landslide Dam ◽  
Remote Sensing Data ◽  
Time Range ◽  
Channel System ◽  
Object Based ◽  
Elevation Model

<p><strong>Abstract.</strong> In August 2009, Typhoon Morakot caused a record-breaking rainfall in Taiwan. The heavy rainfall triggered thousands of landslides, in particular in the central-southern part of the island. Large landslides can block rivers and can lead to the formation of landslide-dammed lakes. Cascading hazards like floods and debris flows after landslide dam breaches pose a high risk for people and infrastructure downstream. Thus, better knowledge about landslides that significantly impact the channel system and about the resulting landslide-dammed lakes are key elements for assessing the direct and indirect hazards caused by the moving mass. The main objectives of this study are 1) to develop an object-based image analysis (OBIA) approach for semi-automated detection of landslides that caused the formation of landslide-dammed lakes and 2) to monitor the evolution of landslide-dammed lakes based on Landsat imagery. For landslide and lake mapping, primarily spectral indices and contextual information were used. By integrating morphological and hydrological parameters derived from a digital elevation model (DEM) into the OBIA framework, we automatically identified landslide-dammed lakes, and the landslides that likely caused the formation of those lakes, due to the input of large amounts of debris into the channel system. The proposed approach can be adapted to other remote sensing platforms and can be used to monitor the evolution of landslide-dammed lakes and triggering landslides at regional scale after typhoon and heavy rainstorm events within an efficient time range after suitable remote sensing data has been provided.</p>

scholarly journals AUTOMATIC BUILDING EXTRACTION USING A DECISION TREE OBJECT-BASED CLASSIFICATION ON JOINT USE OF AERIAL AND LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 429-434
Author(s):  
R. Ghasemi Nejad ◽  
P. Pahlavani ◽  
B. Bigdeli
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Spatial Databases ◽  
Remote Sensing Data ◽  
Predictive Performance ◽  
Single Step ◽  
Building Extraction ◽  
Digital Maps ◽  
Sensing Data ◽  
Object Based

Abstract. Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system is not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective “trial-and-error” process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this method, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance.

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