Small Scale Crater Detection based on Deep Learning with Multi-Temporal Samples of High-Resolution Images

2019 ◽  
Author(s):  
Yanmin Jin ◽  
Fan He ◽  
Shijie Liu ◽  
Xiaohua Tong
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Small Scale ◽  
Crater Detection ◽  
Multi Temporal ◽  
High Resolution Images

scholarly journals Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

2020 ◽  
Vol 12 (7) ◽  
pp. 1144
Author(s):  
Rosa Aguilar ◽  
Monika Kuffer
Keyword(s):  
High Resolution ◽  
Open Space ◽  
Local Planning ◽  
Open Spaces ◽  
High Resolution Imagery ◽  
Coarse Spatial Resolution ◽  
Multi Temporal ◽  
High Resolution Images ◽  
Cloud Computation

Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

High-Resolution Imaging of the Ocean Surface Backscatter by Inversion of Altimeter Waveforms

2011 ◽  
Vol 28 (8) ◽  
pp. 1050-1062 ◽  
Author(s):  
Jean Tournadre ◽  
Bertrand Chapron ◽  
Nicolas Reul
Keyword(s):  
High Resolution ◽  
Significant Wave Height ◽  
Basic Assumption ◽  
Sea Surface ◽  
Small Scale ◽  
High Resolution Imaging ◽  
Small Islands ◽  
Backscatter Coefficient ◽  
High Resolution Images ◽  
Resolution Imaging

Abstract This paper presents a new method to analyze high-resolution altimeter waveforms in terms of surface backscatter. Over the ocean, a basic assumption of modeling altimeter echo waveforms is to consider a homogeneous sea surface within the altimeter footprint that can be described by a mean backscatter coefficient. When the surface backscatter varies strongly at scales smaller than the altimeter footprint size, such as in the presence of surface slicks, rain, small islands, and altimeter echoes can be interpreted as high-resolution images of the surface whose geometry is annular and not rectangular. A method based on the computation of the imaging matrix and its pseudoinverse to infer the surface backscatter at high resolution (~300 m) from the measured waveforms is presented. The method is tested using synthetic waveforms for different surface backscatter fields and is shown to be unbiased and accurate. Several applications can be foreseen to refine the analysis of rain patterns, surface slicks, and lake surfaces. The authors choose here to focus on the small-scale variability of backscatter induced by a submerged reef smaller than the altimeter footprint as the function of tide, significant wave height, and wind.

scholarly journals Accelerating Super-Resolution and Visual Task Analysis in Medical Images

2020 ◽  
Vol 10 (12) ◽  
pp. 4282
Author(s):  
Ghada Zamzmi ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Task Analysis ◽  
Multiple Scales ◽  
Medical Images ◽  
Computational Cost ◽  
Super Resolution ◽  
Visual Task ◽  
Learning Networks ◽  
High Resolution Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.

scholarly journals Improved Mask R-CNN for Rural Building Roof Type Recognition from UAV High-Resolution Images: A Case Study in Hunan Province, China

2022 ◽  
Vol 14 (2) ◽  
pp. 265
Author(s):  
Yanjun Wang ◽  
Shaochun Li ◽  
Fei Teng ◽  
Yunhao Lin ◽  
Mengjie Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Edge Detection ◽  
Hunan Province ◽  
Building Roof ◽  
Rural Buildings ◽  
High Resolution Images ◽  
Rgb Images ◽  
Sobel Edge Detection ◽  
Model Recognition

Accurate roof information of buildings can be obtained from UAV high-resolution images. The large-scale accurate recognition of roof types (such as gabled, flat, hipped, complex and mono-pitched roofs) of rural buildings is crucial for rural planning and construction. At present, most UAV high-resolution optical images only have red, green and blue (RGB) band information, which aggravates the problems of inter-class similarity and intra-class variability of image features. Furthermore, the different roof types of rural buildings are complex, spatially scattered, and easily covered by vegetation, which in turn leads to the low accuracy of roof type identification by existing methods. In response to the above problems, this paper proposes a method for identifying roof types of complex rural buildings based on visible high-resolution remote sensing images from UAVs. First, the fusion of deep learning networks with different visual features is investigated to analyze the effect of the different feature combinations of the visible difference vegetation index (VDVI) and Sobel edge detection features and UAV visible images on model recognition of rural building roof types. Secondly, an improved Mask R-CNN model is proposed to learn more complex features of different types of images of building roofs by using the ResNet152 feature extraction network with migration learning. After we obtained roof type recognition results in two test areas, we evaluated the accuracy of the results using the confusion matrix and obtained the following conclusions: (1) the model with RGB images incorporating Sobel edge detection features has the highest accuracy and enables the model to recognize more and more accurately the roof types of different morphological rural buildings, and the model recognition accuracy (Kappa coefficient (KC)) compared to that of RGB images is on average improved by 0.115; (2) compared with the original Mask R-CNN, U-Net, DeeplabV3 and PSPNet deep learning models, the improved Mask R-CNN model has the highest accuracy in recognizing the roof types of rural buildings, with F1-score, KC and OA averaging 0.777, 0.821 and 0.905, respectively. The method can obtain clear and accurate profiles and types of rural building roofs, and can be extended for green roof suitability evaluation, rooftop solar potential assessment, and other building roof surveys, management and planning.

Urban Forest Identification from High-Resolution Images Using Deep-Learning Method

2021 ◽  
Author(s):  
Wei Wang ◽  
Rongyuan Liu ◽  
Huiyun Yang ◽  
Ping Zhou ◽  
Xiangwen Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Urban Forest ◽  
Learning Method ◽  
High Resolution Images

Fine Magnification of Solar Small-Scale Structures in NVST High-Resolution Images

2020 ◽  
Vol 40 (9) ◽  
pp. 0910002
Author(s):  
王笑笑 Wang Xiaoxiao ◽  
尚振宏 Shang Zhenhong ◽  
强振平 Qiang Zhenping
Keyword(s):  
High Resolution ◽  
Small Scale ◽  
High Resolution Images ◽  
Scale Structures ◽  
Small Scale Structures

Understanding multi-temporal urban forest cover using high resolution images

2018 ◽  
Vol 29 ◽  
pp. 106-112 ◽  
Author(s):  
Aline Canetti ◽  
Marilice Cordeiro Garrastazu ◽  
Patrícia Póvoa de Mattos ◽  
Evaldo Muñoz Braz ◽  
Sylvio Pellico Netto
Keyword(s):  
High Resolution ◽  
Forest Cover ◽  
Urban Forest ◽  
Multi Temporal ◽  
High Resolution Images

scholarly journals Deep learning for enhancing multi-source reverse time migration

2022 ◽  
Author(s):  
Yaxing Li ◽  
Xiaofeng Jia ◽  
Xinming Wu ◽  
Zhicheng Geng
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Plane Wave ◽  
Computational Efficiency ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Numerical Examples ◽  
Time Migration ◽  
High Resolution Images ◽  
Wave Migration

<p>Reverse time migration (RTM) is a technique used to obtain high-resolution images of underground reflectors; however, this method is computationally intensive when dealing with large amounts of seismic data. Multi-source RTM can significantly reduce the computational cost by processing multiple shots simultaneously. However, multi-source-based methods frequently result in crosstalk artifacts in the migrated images, causing serious interference in the imaging signals. Plane-wave migration, as a mainstream multi-source method, can yield migrated images with plane waves in different angles by implementing phase encoding of the source and receiver wavefields; however, this method frequently requires a trade-off between computational efficiency and imaging quality. We propose a method based on deep learning for removing crosstalk artifacts and enhancing the image quality of plane-wave migration images. We designed a convolutional neural network that accepts an input of seven plane-wave images at different angles and outputs a clear and enhanced image. We built 505 1024×256 velocity models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the network. Labels are high-resolution images computed from the corresponding reflectivity models by convolving with a Ricker wavelet. Random sub-images with a size of 512×128 were used for training the network. Numerical examples demonstrated the effectiveness of the trained network in crosstalk removal and imaging enhancement. The proposed method is superior to both the conventional RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed method requires only seven migrations, significantly improving the computational efficiency. In the numerical examples, the processing time required by our method was approximately 1.6% and 10% of that required by RTM and PWRTM, respectively.</p>

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