Modeling image interpretation in remote sensing through a virtual GIS shell (VGIS)

1994 ◽  
Author(s):  
Manfred Ehlers ◽  
Hartmut Broesamle ◽  
Jochen Albrecht
Keyword(s):  
Remote Sensing ◽  
Image Interpretation

scholarly journals PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1243
Author(s):  
Wenxin Yin ◽  
Wenhui Diao ◽  
Peijin Wang ◽  
Xin Gao ◽  
Ya Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Power Plants ◽  
State Of The Art ◽  
Thermal Power ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Thermal Power Plants ◽  
Average Precision ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale

The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

scholarly journals Multiple Pathways: The Influence Mechanism of Greenspace Exposure on Mental Health—A Case Study of Hangzhou, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 339
Author(s):  
Jiancheng Lu ◽  
Xiaolong Luo ◽  
Ningning Yang ◽  
Yang Shen
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Remote Sensing ◽  
Structural Equation ◽  
Positive Impact ◽  
Personal Information ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Equation Model ◽  
Influence Mechanism

Greenspace exposure (GSE) may have a positive impact on mental health. However, existing research lacks a classification analysis of the influence pathways of different GSE on mental health. Meanwhile, the research method is limited to the measurement of the green space ratio (GSR) based on remote sensing data, which ignores people’s real perception of greenspace. This paper aims to further expand the measurement method of GSE, taking Hangzhou, China as an example, and to reveal the influence mechanism of different GSE modes on mental health. We obtained the personal information, mental health, physical activity, and other data of the interviewees through a questionnaire (n = 461). Combined with a remote sensing satellite and the Baidu Street view database, the method of image interpretation and deep learning was used to obtain the GSR, green visual ratio (GVR), and green visual exposure (GVE). The structural equation model is used to analyze the relationship between different variables. The results showed that: (1) GSE has a certain positive effect on mental health; (2) there are differences in the influence mechanism of multiple measures of GSE on mental health—the GVR and GVE measures based on the interaction perspective between human and greenspace make the influence mechanism more complicated, and produce direct and indirect influence paths; and (3) greenspace perception, sense of community, and physical activity can act as mediators, and have indirect effects. Finally, we call for expanding the measurement index and methods of GSE and integrating them into the management and control practices of urban planning to promote the healthy development of communities and even cities.

Active fusion - A new method applied to remote sensing image interpretation

1996 ◽  
Vol 17 (13) ◽  
pp. 1349-1359 ◽  
Author(s):  
Axel Pinz ◽  
Manfred Prantl ◽  
Harald Ganster ◽  
Hermann Kopp-Borotschnig
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
New Method

Changing Role of Image Interpretation

1971 ◽  
Vol 25 (4) ◽  
pp. 477-453
Author(s):  
Zdenek D. Kalensky
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Data Recording ◽  
Rapid Progress ◽  
Educational Facilities ◽  
Related Field ◽  
Stage Scheme ◽  
Changing Role ◽  
Adequate Research

Image interpretation used to be, and often still is, a marginal specialization within many professions. Because of a lack of adequate research and educational facilities, it has been poorly equipped to cope with the rapid progress in the related field of remote sensing. As a consequence, a large disparity exists between the rate of data recording and their utilization. To rectify this situation, image interpretation should be a discipline in its own right. The emphasis must shift from applications, which should remain in the domain of professionals in disciplines using the data, to fundamental aspects which are common to all disciplines. A two-stage scheme of image interpretation based on teamwork between an image analyst and a specialist in a particular field of application is discussed, so too is its impact on the role of image interpretation.

scholarly journals Impact of beel encroachment on aquatic biodiversity and vulnerability of poor fishermen in Noagaon District, Bangladesh

2020 ◽  
Vol 6 (2) ◽  
pp. 244-254
Author(s):  
MA Salam ◽  
KM Shakil Rana ◽  
Md Touhidur Rahman
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Group Living ◽  
Secondary Data ◽  
Data Interpretation ◽  
Water Area ◽  
Population Pressure ◽  
Aquatic Biodiversity ◽  
Fish Biodiversity ◽  
Over Time

Natural water bodies in Bangladesh are under threat of encroachment due to high population pressure, overexploitation, change of watercourses, and siltation. Therefore, the present study carried out to assess the degree of encroachment of floodplains and its impact on fish biodiversity and the livelihoods of the neighboring fishing communities. The study covered three beels of Naogaon district through remote sensing image interpretation and PRA techniques. The study used three dates remote sensing images and field data which supplemented with the secondary data from diverse origins. Data collected through the recalling method, personal interview with a structured questionnaire and livelihood analysis of fishermen and non-fishermen group living around the beels. The data interpretation showed the water area reduced by 80% from 1981 to 2016 in dry season that converted to boro rice cultivation gradually over time. The type of fishing gears and their use also changed radically over time in all the three beels. The fish catch increased steadily from 1981 and reached its peak in 1996, and then started to fall and continued up to 2016 in all the three beels. The study identified thirty species of SIS and SRS in 1981 that were gradually reduced to 6, 8 and 9 in Digholi, Fatepur and Pakuria beels respectively in 2016. Fish biodiversity reduced as fish-friendly large mesh cotton net replaced by the smaller mesh jagotber jal and monofilament synthetic current jal. Moreover, the annual income of the fishermen family was lower than non- fishermen family in beels areas. The current study clearly identified significant encroachment of floodplains area by agricultural activities between 1981 and 2016 and aquatic biodiversity reduced dramatically and the livelihoods of poor fishermen became vulnerable. Asian J. Med. Biol. Res. June 2020, 6(2): 244-254

Study on Macroscopically Dynamic Monitoring of Newly Increased Construction Land in Northwestern Plains Based on Middle Resolution Remote Sensing Data

2013 ◽  
Vol 333-335 ◽  
pp. 1475-1478
Author(s):  
Zhi Hong Liu ◽  
Xing Ke Yang ◽  
Qian Zhu ◽  
Hu Jun He ◽  
San You Cheng
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Dynamic Monitoring ◽  
Remote Sensing Images ◽  
Construction Land ◽  
Multi Temporal ◽  
Northwestern Region ◽  
Northwestern Plains

Analyzing the significance of macroscopically dynamic monitoring of newly increased construction land, and considering the influence of various factors, this paper selects central Shaanxi Plain in Northwestern region for a typical experimental zone, setting up knowledge base of remote sensing images interpretation, using multi-temporal remote sensing images, carrying through interactive interpretation of change patterns spots of newly increased construction land and field validation. Results of middle resolution remote sensing image interpretation are compared, analyzed. Additionally, interpretation accuracy of different scales are studied, especially between middle resolution 10 ms ALOS remote sensing image and panchromatic high resolution remote sensing, on newly increased construction land in northwestern plains, to find out the remote sensing images which can not only quickly extract new construction land change patterns spots, but also can satisfy precision requirement of the business.

scholarly journals Pemetaan Kawasan Lahan Pertanian Pangan Berkelanjutan di Kuta Utara dan Mengwi Berbasis Penginderaan Jauh dan Sistem Informasi Geografis

2021 ◽  
Vol 11 (2) ◽  
pp. 176
Author(s):  
KARTIKA DEWI OKTAFIANTI ◽  
INDAYATI LANYA ◽  
NI MADE TRIGUNASIH
Keyword(s):  
Remote Sensing ◽  
Agricultural Land ◽  
Field Survey ◽  
Spatial Information ◽  
Image Interpretation ◽  
Rice Fields ◽  
Geographical Information ◽  
Sustainable Food ◽  
Physical Conditions

Mapping of Sustainable Food Agricultural Land at North Kuta and Mengwi Districts Based on Remote Sensing and Geographical Information System. Sustainable Food Agricultural Land (LP2B) is a field of agricultural land designated to be protected and developed consistently in order to produce staple food for national food independence, resilience and sovereignty. The Badung Regency Government has determined the area and location of LP2B but it has not been accompanied by a spatial information map. This study aims to map subak rice fields in 2019 as well as mapping of LP2B based on the physical conditions of the area and the environment in North Kuta and Mengwi Districts based on remote sensing and GIS. The method used consists of image interpretation, field survey and numerical classification. The results showed that the distribution of subak rice fields in North Kuta and Mengwi Districts was 4967.22 ha. The distribution of rice fields in North Kuta District is 850.15 ha and in Mengwi District is 4117.07 ha. In the classification of LP2B areas, the recommended area is model 1 (234.88 ha), model 2 (939.76 ha) and model 3 (2048.63 ha). The recommendation areas are in model 1 (1489.91 ha), model 2 (1101.52 ha) and model 3 (2047.53 ha). The conditional recommendation area is in model 1 (2969.50 ha), model 2 (2048.49 ha) and model 3 (873.39 ha). Not recommended area in model 1 (270.81 ha), model 2 (875.33 ha) and model 3 (0 ha).

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