scholarly journals Object-based Integrated Landscape Change Analysis: synergy of multi-temporal LiDAR and very high resolution orthophotos

10.3990/2.397 ◽  
2016 ◽  
Author(s):  
M. Kamps ◽  
A.C. Seijmonsbergen ◽  
W. Bouten
Keyword(s):  
High Resolution ◽  
Landscape Change ◽  
Change Analysis ◽  
Object Based ◽  
Multi Temporal ◽  
Very High

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

scholarly journals High Resolution Landscape Change Analysis with CORONA KH-4B Imagery. A Case Study from Iron Gates Reservoir Area

2016 ◽  
Vol 32 ◽  
pp. 200-210 ◽  
Author(s):  
Bogdan Mihai ◽  
Constantin Nistor ◽  
Liviu Toma ◽  
Ionuţ Săvulescu
Keyword(s):  
High Resolution ◽  
Landscape Change ◽  
Change Analysis ◽  
Reservoir Area

scholarly journals On the potential of very high-resolution repeat DEMs in glacial and periglacial environments

2010 ◽  
Vol 4 (1) ◽  
pp. 53-65 ◽  
Author(s):  
J. Abermann ◽  
A. Fischer ◽  
A. Lambrecht ◽  
T. Geist
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Airborne Lidar ◽  
Primary Data ◽  
Glacier Area ◽  
Airborne Laser ◽  
Multi Temporal ◽  
Rock Glaciers ◽  
Data Source ◽  
Very High

Abstract. The potential of high-resolution repeat DEMs was investigated for glaciological applications including periglacial features (e.g. rock glaciers). It was shown that glacier boundaries can be delineated using airborne LIDAR-DEMs as a primary data source and that information on debris cover extent could be extracted using multi-temporal DEMs. Problems and limitations are discussed, and accuracies quantified. Absolute deviations of airborne laser scanning (ALS) derived glacier boundaries from ground-truthed ones were below 4 m for 80% of the ground-truthed values. Overall, we estimated an accuracy of +/−1.5% of the glacier area for glaciers larger than 1 km2. The errors in the case of smaller glaciers did not exceed +/−5% of the glacier area. The use of repeat DEMs in order to obtain information on the extent, characteristics and activity of rock glaciers was investigated and discussed based on examples.

Synergy of very high resolution optical and radar data for object-based olive grove mapping

2011 ◽  
Vol 25 (6) ◽  
pp. 971-989 ◽  
Author(s):  
Jan Peters ◽  
Frieke Van Coillie ◽  
Toon Westra ◽  
Robert De Wulf
Keyword(s):  
High Resolution ◽  
Radar Data ◽  
Olive Grove ◽  
Object Based ◽  
Very High

scholarly journals CNN BASED DETECTION OF BUILDING ROOFS FROM HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 187-192
Author(s):  
L. Hang ◽  
G. Y. Cai
Keyword(s):  
High Resolution ◽  
Residential Building ◽  
Training Image ◽  
Training Dataset ◽  
Classification Approach ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Different Types ◽  
Model Training ◽  
Very High

Abstract. The detection and reconstruction of building have attracted more attention in the community of remote sensing and computer vision. Light detection and ranging (LiDAR) has been proved to be a good way to extract building roofs, while we have to face the problem of data shortage for most of the time. In this paper, we tried to extract the building roofs from very high resolution (VHR) images of Chinese satellite Gaofen-2 by employing convolutional neural network (CNN). It has been proved that the CNN is of a higher capability of recognizing detailed features which may not be classified out by object-based classification approach. Several major steps are concerned in this study, such as generation of training dataset, model training, image segmentation and building roofs recognition. First, urban objects such as trees, roads, squares and buildings were classified based on random forest algorithm by an object-oriented classification approach, the building regions were separated from other classes at the aid of visually interpretation and correction; Next, different types of building roofs mainly categorized by color and size information were trained using the trained CNN. Finally, the industrial and residential building roofs have been recognized individually and the results have been validated individually. The assessment results prove effectiveness of the proposed method with approximately 91% and 88% of quality rates in detection industrial and residential building roofs, respectively. Which means that the CNN approach is prospecting in detecting buildings with a very higher accuracy.

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