scholarly journals A Content-Based Remote Sensing Image Change Information Retrieval Model

2017 ◽  
Author(s):  
Caihong Ma ◽  
Wei Xia ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Qin Dai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Retrieval ◽  
Change Detection ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Prototype System ◽  
Retrieval Model ◽  
Model Framework ◽  
New Model

With the rapid development of satellite remote sensing technology, the volume of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval in a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing, deal with problems related toseasonal changes as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval.

Removing Cloud of Grey Remote Sensing Image Based on Network Communication

2014 ◽  
Vol 513-517 ◽  
pp. 3165-3169
Author(s):  
Min Min Yue
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Cloud Formation ◽  
Network Communication ◽  
Fusion Model ◽  
Modern Computer ◽  
Model Combining ◽  
Set Up

Remote sensing technology has rapid development in the past half one century, it is widely used in various fields and society. But the clouds have affected the quality of remote sensing data, how to effectively use the modern computer science and technology to remove the cloud is a hot issue in the field. From the theory of cloud formation in the remote sensing image, we analyze the formation mechanism, and based on this we do two layers decomposition and reconstruct the structure according to wavelet transform in network communication, and establish the image degradation model. Combining Fourier transformation, we set up the removing cloud fusion model of remote sensing image. Through the simulation experiment, the effect is significant. To a certain extent, it provides technical support for theory study and practice operation.

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 Content-Based Remote Sensing Image Change Information Retrieval Model

2017 ◽  
Vol 6 (10) ◽  
pp. 310 ◽  
Author(s):  
Caihong Ma ◽  
Wei Xia ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Qin Dai ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Retrieval ◽  
Remote Sensing Image ◽  
Retrieval Model

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 FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 243-249
Author(s):  
P. Ebel ◽  
S. Saha ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Optical Observations ◽  
Detection Methods ◽  
Data Set ◽  
Modal Data ◽  
Multi Temporal ◽  
Modal Change

Abstract. With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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