Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation

2021 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Jiaqi Zhao ◽  
Yong Zhou ◽  
Boyu Shi ◽  
Jingsong Yang ◽  
Di Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Sensor Technology ◽  
Feature Maps ◽  
Attention Network ◽  
Modal Data ◽  
Sensing Data ◽  
Multi Stage

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS) 2 -Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method.

scholarly journals A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

2021 ◽  
Vol 33 (6) ◽  
pp. 1-20
Author(s):  
Hui Lu ◽  
Qi Liu ◽  
Xiaodong Liu ◽  
Yonghong Zhang
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Semantic Knowledge ◽  
Learning Technology ◽  
Semantic Classification ◽  
Sensing Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

scholarly journals A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Semantic Knowledge ◽  
Learning Technology ◽  
Semantic Classification ◽  
Sensing Data

With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention. Through the semantic research of remote sensing data, the processing ability of remote sensing data is greatly improved. This paper aims to introduce and analyze the research and application progress of remote sensing image satellite data processing from the perspective of semantic. Firstly, it introduces the characteristics and semantic knowledge of remote sensing big data; Secondly, the semantic concept, semantic construction and application fields are introduced in detail; then, for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic classification and semantic search, focusing on deep learning technology; Finally, the problems and challenges in the four aspects are discussed in detail, in order to find more directions to explore.

scholarly journals Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3132
Author(s):  
Emmanouil A. Varouchakis ◽  
Anna Kamińska-Chuchmała ◽  
Grzegorz Kowalik ◽  
Katerina Spanoudaki ◽  
Manuel Graña
Keyword(s):  
Remote Sensing ◽  
Wide Spectrum ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Spatiotemporal Analysis ◽  
Space Time ◽  
Attractive Alternative ◽  
Kriging Method ◽  
Sensing Data ◽  
Crete Island

The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

scholarly journals Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Pham Vu Dong ◽  
Bui Quang Thanh ◽  
Nguyen Quoc Huy ◽  
Vo Hong Anh ◽  
Pham Van Manh
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Rapid Development ◽  
Open Data ◽  
Remote Sensing Data ◽  
Data Cube ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

scholarly journals Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data

2021 ◽  
Vol 13 (18) ◽  
pp. 3710
Author(s):  
Abolfazl Abdollahi ◽  
Biswajeet Pradhan ◽  
Nagesh Shukla ◽  
Subrata Chakraborty ◽  
Abdullah Alamri
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Urban Areas ◽  
Object Segmentation ◽  
Remote Sensing Data ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Urban Scenes ◽  
Sensing Data ◽  
Boundary Information

Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work’s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks.

scholarly journals A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

2019 ◽  
Vol 8 (12) ◽  
pp. 533 ◽  
Author(s):  
Shuang Wang ◽  
Guoqing Li ◽  
Xiaochuang Yao ◽  
Yi Zeng ◽  
Lushen Pang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Relational Databases ◽  
Large Scale ◽  
Distributed Storage ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Image Data ◽  
Sensing Data ◽  
Cluster Architecture ◽  
Distributed Cluster

With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

scholarly journals Role of Emissivity in Lava Flow ‘Distance-to-Run’ Estimates from Satellite-Based Volcano Monitoring

2019 ◽  
Vol 11 (6) ◽  
pp. 662 ◽  
Author(s):  
Nikola Rogic ◽  
Annalisa Cappello ◽  
Fabrizio Ferrucci
Keyword(s):  
Remote Sensing ◽  
Lava Flow ◽  
Land Surface ◽  
Risk Mitigation ◽  
Flow Behavior ◽  
Remote Sensing Data ◽  
Mount Etna ◽  
Thermal Anomalies ◽  
Sensing Data ◽  
Multi Stage

Remote sensing is an established technological solution for bridging critical gaps in volcanic hazard assessment and risk mitigation. The enormous amount of remote sensing data available today at a range of temporal and spatial resolutions can aid emergency management in volcanic crises by detecting and measuring high-temperature thermal anomalies and providing lava flow propagation forecasts. In such thermal estimates, an important role is played by emissivity—the efficiency with which a surface radiates its thermal energy at various wavelengths. Emissivity has a close relationship with land surface temperatures and radiant fluxes, and it impacts directly on the prediction of lava flow behavior, as mass flux estimates depend on measured radiant fluxes. Since emissivity is seldom measured and mostly assumed, we aimed to fill this gap in knowledge by carrying out a multi-stage experiment, combining laboratory-based Fourier transform infrared (FTIR) analyses, remote sensing data, and numerical modeling. We tested the capacity for reproducing emissivity from spaceborne observations using ASTER Global Emissivity Database (GED) while assessing the spatial heterogeneity of emissivity. Our laboratory-satellite emissivity values were used to establish a realistic land surface temperature from a high-resolution spaceborne payload (ETM+) to obtain an instant temperature–radiant flux and eruption rate results for the 2001 Mount Etna (Italy) eruption. Forward-modeling tests conducted on the 2001 ‘aa’ lava flow by means of the MAGFLOW Cellular Automata code produced differences of up to ~600 m in the simulated lava flow ‘distance-to-run’ for a range of emissivity values. Given the density and proximity of urban settlements on and around Mount Etna, these results may have significant implications for civil protection and urban planning applications.

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