scholarly journals A Feasibility Study of Sea Ice Motion and Deformation Measurements Using Multi-Sensor High-Resolution Optical Satellite Images

2017 ◽  
Vol 9 (9) ◽  
pp. 930 ◽  
Author(s):  
◽  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Feasibility Study ◽  
Satellite Images ◽  
Optical Satellite Images ◽  
Deformation Measurements

scholarly journals Characterizing the Sea-Ice Floe Size Distribution in the Canada Basin from High-Resolution Optical Satellite Imagery

2021 ◽  
Author(s):  
Alexis Anne Denton ◽  
Mary-Louise Timmermans
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Size Distribution ◽  
Satellite Images ◽  
Threshold Value ◽  
Canada Basin ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Optical Satellite Images

Abstract. The sea-ice floe size distribution (FSD) characterizes the sea-ice response to atmosphere and ocean forcing and is important for understanding and modeling the evolving ice pack in a warming Arctic. FSDs are evaluated from 78 floe- segmented high-resolution (1-m) optical satellite images capturing a range of settings and sea-ice states during spring through fall from 1999 to 2014 in the Canada Basin. For any given image, the structure of the FSD is found to be sensitive to a classification threshold value (i.e., to specify an image pixel as being either water or ice) used in image segmentation, and an objective approach to minimize this sensitivity is presented. The FSDs are found to exhibit a single power-law regime between floe areas 50 m2 and 5 km2, characterized by exponents (slopes in log-log space) in the range −2.03 to −1.65. A distinct linear relationship between slopes and sea-ice concentrations is found, with steeper slopes (i.e., a larger proportion of smaller to larger floes) corresponding to lower sea-ice concentrations. Further, a seasonal variation in slopes is found for fixed sites in the Canada Basin that undergo a seasonal cycle in sea-ice concentration, while sites with extensive sea-ice cover year-round do not exhibit any seasonal change in FSD properties. Our results suggest that sea-ice concentration should be considered in any characterization of a time-varying FSD (for use in sea-ice models, for example).

Toward a general tropical forest biomass prediction model from very high resolution optical satellite images

2017 ◽  
Vol 200 ◽  
pp. 140-153 ◽  
Author(s):  
P. Ploton ◽  
N. Barbier ◽  
P. Couteron ◽  
C.M. Antin ◽  
N. Ayyappan ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Model ◽  
Tropical Forest ◽  
Satellite Images ◽  
Forest Biomass ◽  
Optical Satellite Images ◽  
Very High

AUTOMATIC REGISTRATION OF VECTOR DATA WITH OPTICAL IMAGES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 191-196
Author(s):  
Y. Tanguy ◽  
J. Michel ◽  
G. Salgues
Keyword(s):  
High Resolution ◽  
Image Registration ◽  
Prior Knowledge ◽  
Open Source Software ◽  
Satellite Images ◽  
Automatic Registration ◽  
Vector Data ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Very High

Abstract. This paper presents a method to perform automatic vector-to-image registration. The proposed method performs well on different kinds of optical satellite images from Very High Resolution (VHR, sub-meter resolution) to images in the 10/20 m resolution range. It allows to automatically register vector dataset such as urban maps (by using building layers). In contrast with existing methods, our method needs few prior-knowledge on the features to match and can therefore adapt to different landscapes.This paper demonstrates the method robustness in several use-cases and presents the implementation which will soon be available as open-source software.

scholarly journals H-YOLO: A Single-Shot Ship Detection Approach Based on Region of Interest Preselected Network

2020 ◽  
Vol 12 (24) ◽  
pp. 4192
Author(s):  
Gang Tang ◽  
Shibo Liu ◽  
Iwao Fujino ◽  
Christophe Claramunt ◽  
Yide Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Google Earth ◽  
Single Shot ◽  
Ship Detection ◽  
Detection Approach ◽  
Optical Satellite Images ◽  
Image Network

Ship detection from high-resolution optical satellite images is still an important task that deserves optimal solutions. This paper introduces a novel high-resolution image network-based approach based on the preselection of a region of interest (RoI). This pre-selected network first identifies and extracts a region of interest from input images. In order to efficiently match ship candidates, the principle of our approach is to distinguish suspected areas from the images based on hue, saturation, value (HSV) differences between ships and the background. The whole approach is the basis of an experiment with a large ship dataset, consisting of Google Earth images and HRSC2016 datasets. The experiment shows that the H-YOLO network, which uses the same weight training from a set of remote sensing images, has a 19.01% higher recognition rate and a 16.19% higher accuracy than applying the you only look once (YOLO) network alone. After image preprocessing, the value of the intersection over union (IoU) is also greatly improved.

scholarly journals CHARACTERISTICS OF TEXTURE INDEX OF DAMAGED BUILDINGS USING TIME-SERIES HIGH-RESOLUTION OPTICAL SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1709-1714
Author(s):  
M. Sonobe
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Texture Analysis ◽  
Large Scale ◽  
Satellite Images ◽  
Image Interpretation ◽  
High Resolution Satellite Images ◽  
Survey Results ◽  
Occurrence Matrix ◽  
Optical Satellite Images

Abstract. A large-scale disaster has occurred due to the earthquake. In particular, 20% of the world's earthquakes with a magnitude of 6 or more occur near Japan. Damage analysis of buildings by image analysis have been effectively carried out using optical high-resolution satellite images and aerial photograph with spatial resolution of about 2 m or less. In this study, the damaged buildings caused by large-scale and continuous earthquakes in Kumamoto, Japan that occurred in April 2016 was selected as a typical example of damaged buildings. For these earthquake event, the applicability of damage distribution of buildings and recovery/restoration status by texture analysis was examined. The applicability of the representative in the dissimilarity texture analysis methods Gray- Level Co-occurrence Matrix (GLCM) method by image interpretation in the case of a large number of collapsed and wrecked buildings in a wide area was assessed. These results suggest that dissimilarity was applicable to the extraction of damaged and removed buildings in the event of such an earthquake. In addition, the analysis results were appropriately evaluated by comparing the field survey results with the image interpretation results of the pan-sharpened image. From these results, we confirmed the effectiveness of texture analysis using time-series high-resolution satellite images in grasping the damaged buildings before and immediately after the disaster and in the restoration situation 1 year after the disaster.

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