scholarly journals Comparison of remote sensing extraction methods for glacier firn line-considering Urumqi Glacier No.1 as the experimental area

2020 ◽  
Vol 218 ◽  
pp. 04024
Author(s):  
Yanjun Zhao ◽  
Jun Zhao ◽  
Xiaoying Yue ◽  
Yanqiang Wang
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Extraction Methods ◽  
High Accuracy ◽  
Single Band ◽  
Visual Interpretation ◽  
Negative Mass ◽  
Inversion Methods ◽  
Experimental Area ◽  
Normalized Difference Snow Index

In mid-latitude glaciers, the altitude of the snowline at the end of the ablating season can be used to indicate the equilibrium line, which can be used as an approximation for it. In this paper, Urumqi Glacier No.1 was selected as the experimental area while Landsat TM/ETM+/OLI images were used to analyze and compare the accuracy as well as applicability of the visual interpretation, Normalized Difference Snow Index, single-band threshold and albedo remote sensing inversion methods for the extraction of the firn lines. The results show that the visual interpretation and the albedo remote sensing inversion methods have strong adaptability, alonger with the high accuracy of the extracted firn line while it is followed by the Normalized Difference Snow Index and the single-band threshold methods. In the year with extremely negative mass balance, the altitude deviation of the firn line extracted by different methods is increased. Except for the years with extremely negative mass balance, the altitude of the firn line at the end of the ablating season has a good indication for the altitude of the balance line.

Comparison of Coastline Extraction Methods and Block Classification Method to Extract Coastlines based on Remote Sensing

2021 ◽  
Author(s):  
Zongmei Li ◽  
Hongmei Chen ◽  
Qin Nie
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Extraction Methods ◽  
Classification Method ◽  
Segmentation Method ◽  
Visual Interpretation ◽  
Threshold Segmentation ◽  
Detection Algorithms ◽  
Block Classification ◽  
Interpretation Method

Abstract Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

scholarly journals Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2524
Author(s):  
Ziyi Chen ◽  
Dilong Li ◽  
Wentao Fan ◽  
Haiyan Guan ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Extraction Methods ◽  
The Self ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

scholarly journals Glacier Surface Mass Balance in the Suntar-Khayata Mountains, Northeastern Siberia

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1949 ◽  
Author(s):  
Yong Zhang ◽  
Xin Wang ◽  
Zongli Jiang ◽  
Junfeng Wei ◽  
Hiroyuki Enomoto ◽  
...  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Surface Mass Balance ◽  
Negative Mass ◽  
Glacier Surface ◽  
Surface Mass ◽  
Northeastern Siberia ◽  
Response To Temperature ◽  
Rapid Mass

Arctic glaciers comprise a small fraction of the world’s land ice area, but their ongoing mass loss currently represents a large cryospheric contribution to the sea level rise. In the Suntar-Khayata Mountains (SKMs) of northeastern Siberia, in situ measurements of glacier surface mass balance (SMB) are relatively sparse, limiting our understanding of the spatiotemporal patterns of regional mass loss. Here, we present SMB time series for all glaciers in the SKMs, estimated through a glacier SMB model. Our results yielded an average SMB of −0.22 m water equivalents (w.e.) year−1 for the whole region during 1951–2011. We found that 77.4% of these glaciers had a negative mass balance and detected slightly negative mass balance prior to 1991 and significantly rapid mass loss since 1991. The analysis suggests that the rapidly accelerating mass loss was dominated by increased surface melting, while the importance of refreezing in the SMB progressively decreased over time. Projections under two future climate scenarios confirmed the sustained rapid shrinkage of these glaciers. In response to temperature rise, the total present glacier area is likely to decrease by around 50% during the period 2071–2100 under representative concentration pathway 8.5 (RCP8.5).

Better Visual Interpretation for Remote Sensing Scene Classification

2021 ◽  
pp. 1-1
Author(s):  
Xu Huang ◽  
Yuxi Sun ◽  
Shanshan Feng ◽  
Yunming Ye ◽  
Xutao Li
Keyword(s):  
Remote Sensing ◽  
Scene Classification ◽  
Visual Interpretation
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