scholarly journals Agricultural climate change based on GIS and remote sensing image and the spatial distribution of sports public services

2021 ◽  
Vol 14 (11) ◽  
Author(s):  
Fan He
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Spatial Distribution ◽  
Public Services ◽  
Remote Sensing Image ◽  
Gis And Remote Sensing

scholarly journals Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval

2020 ◽  
Vol 9 (2) ◽  
pp. 61
Author(s):  
Hongwei Zhao ◽  
Lin Yuan ◽  
Haoyu Zhao
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Image Retrieval ◽  
Learning Strategy ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Retention Loss ◽  
Deep Metric Learning ◽  
Hard Samples

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.

scholarly journals STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE

2016 ◽  
Vol XLI-B2 ◽  
pp. 305-312
Author(s):  
Weili Jiao ◽  
Tengfei Long ◽  
Saiguang Ling ◽  
Guojin He
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Traditional Method ◽  
Evaluation Method ◽  
Control Point ◽  
Remote Sensing Image ◽  
Error Distribution ◽  
Distribution Model ◽  
Control Points ◽  
Mean Square Errors

It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.

FORECAST EFFECTS OF DROUGHT ON AGRICULTURAL LAND USE BY GIS AND REMOTE SENSING IN BAC TRA MY DISTRICT, QUANG NAM PROVINCE

2021 ◽  
Vol 130 (3C) ◽  
Author(s):  
Tran Thi Phuong ◽  
Nguyen Bich Ngoc ◽  
Nguyen Hoang Khanh Linh ◽  
Nguyen Thi Hong Mai ◽  
Huynh Van Chuong
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Agricultural Land ◽  
Geographical Information ◽  
Agricultural Land Use ◽  
Severe Drought ◽  
Gis And Remote Sensing ◽  
Rcp 8.5 ◽  
Effects Of Drought

The phenomenon of prolonged drought as one of the consequences of climate change has significantly affected the agricultural production of rural communities in both mountainous and plain areas of Vietnam. This study, using standardized precipitation index (SPI) combining with the space technologies of Geographical Information Systems (GIS) and Remote Sensing (RS) to simulate and forecast the effects of drought on agricultural land use in Bac Tra My district, Quang Nam province. The data was set up for two scenarios of RCP 4.5 and RCP 8.5 in Bac Tra My district of Quang Nam province. Simultaneously, the research has also applied the focus group discussion, in-depth interview and field survey for data cross-checking to ensure highly reliable predictions. The research result has addressed four levels of drought, including normal, mild, moderate and severe drought appearing in the Summer-Autumn crop in the period 2016 – 2035 of the district. In which, severe drought will appear on large scale for both scenarios of RCP 4.5 and RCP 8.5 for 5 types of agricultural land use including paddy, annual crop, perennial, afforestation and aquacultural land. From these findings, the local authorities can consider and apply the adaptation and mitigation measures to climate change in agricultural land use planning.

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