Research on Extracting Residential Information from ETM+Images - A Case Study of Jiaxing City

2012 ◽  
Vol 226-228 ◽  
pp. 2103-2106
Author(s):  
Jun Nan Xiong ◽  
Shan Liu ◽  
Yan Mei Yang ◽  
Ze Gen Wang
Keyword(s):  
Remote Sensing ◽  
City Planning ◽  
Research Area ◽  
Residential Area ◽  
Spectral Structure ◽  
Disaster Assessment ◽  
Result Show ◽  
Remote Sensing Mapping ◽  
Extraction Model

Extracting residential area from remote sensing image is an important issue in remote sensing mapping, disaster assessment, city planning and other field. In this paper, the existing residential area extraction method are analyzed at first, including the method of threshold structure in the spectral, combination knowledge discovery and spectral structure, NDBI, NBI and IBI ; Then, the differences of Spectral characteristic and how to distinguish residential area and other land use type are discussed, the extraction model is build based on the above research. Taking Jiaxing City, Zhejiang Province as an research area, residential area are extracted from ETM+ image use NDBI index and Spectral structure difference. At last, precision analysis indicate location accuracy is 85% ,area accuracy is 88.8%, the result show this method can improve the divisibility of residential area, bare land and road. The extraction result can provide data for remote sensing mapping, disaster assessment, city planning, etc.

scholarly journals The Utilization of ALOS PALSAR Image to Estimate Natural Forest Biomass: Case Study at Bogani Nani Wartabone National Park

Jurnal Wasian ◽  
2014 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Nurlita Indah Wahyuni
Keyword(s):  
Remote Sensing ◽  
National Park ◽  
Satellite Image ◽  
Forest Biomass ◽  
Research Area ◽  
Natural Forest ◽  
Alos Palsar ◽  
Remote Sensing Technology ◽  
Sensing Technology

The development of remote sensing technology makes it possible to utilize its data in many sectors including forestry. Remote sensing image has been used to map land cover and monitor deforestation. This paper presents utilization of ALOS PALSAR image to estimate and map aboveground biomass at natural forest of Bogani Nani Wartabone National Park especially SPTN II Doloduo and SPTN III Maelang. We used modeling method between biomass value from direct measurement and digital number of satellite image. There are two maps which present the distribution of biomass and carbon from ALOS PALSAR image with 50 m spatial resolution. These maps were built based on backscatter polarization of HH and HV bands. The maps indicate most research area dominated with biomass stock 0-5.000 ton/ha.

scholarly journals An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

scholarly journals Urban Black-Odor Water Remote Sensing Mapping Based on Shadow Removal: A Case Study in Nanjing

2021 ◽  
Vol 14 ◽  
pp. 9584-9596
Author(s):  
Taixia Wu ◽  
Mengyao Li ◽  
Shudong Wang ◽  
Yingying Yang ◽  
Shan Sang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shadow Removal ◽  
Remote Sensing Mapping

scholarly journals An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

scholarly journals GIS-supported evaluation of landslide susceptibility in the karst mountainous area：A case study in Wudang, Guiyang

2020 ◽  
Vol 143 ◽  
pp. 02032
Author(s):  
Song Li ◽  
Yi Bai ◽  
Yongjun Long ◽  
Maoqiang Wang
Keyword(s):  
Remote Sensing ◽  
Rural Areas ◽  
Research Area ◽  
Controlling Factors ◽  
Guizhou Province ◽  
Regional Security ◽  
Mountainous Area ◽  
Landsat Images ◽  
Medium Risk

Landslide is the main disaster in the mountainous area. Based on landslide information content models of remote sensing, the work used the aerial and space remote sensing of UltraCamXp WA, Beijing-1 and Landsat images in Wudang, Guiyang to obtain the relative relief, slope, curvature of bedding slope, LUCC, geology and TWI. Finally, we analyzed the spatial susceptibility in the research area. Results showed that there were 42, 56 and 46 potential landslide groups in the high, higher and medium risk regions. The controlling factors of landslides in Wudang, Guiyang refer to the precipitation and precipitation intensity. The densely-populated regions also have the high risk of landslide, and the risk of landslide generally decreases from cities to rural areas. Through the space prediction research of landslide disasters, it is expected to provide valuable protection for regional security and harmonious development, and then sustainable development of Guizhou Province.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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