Analysing the Capability of NCI Technique in Change Detection Using High- and Medium-Resolution Multispectral Data

2020 ◽  
pp. 133-147
Author(s):  
Subhanil Guha ◽  
Himanshu Govil ◽  
Anindita Dey ◽  
Neetu Gill
Keyword(s):  
Change Detection ◽  
Multispectral Data ◽  
Medium Resolution

scholarly journals FEASIBILITY OF MULTISPECTRAL AIRBORNE LASER SCANNING FOR LAND COVER CLASSIFICATION, ROAD MAPPING AND MAP UPDATING

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 119-122 ◽  
Author(s):  
L. Matikainen ◽  
K. Karila ◽  
J. Hyyppä ◽  
E. Puttonen ◽  
P. Litkey ◽  
...  
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Land Cover Classification ◽  
Aerial Images ◽  
Multispectral Data ◽  
Airborne Laser ◽  
First Results ◽  
Road Mapping

This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015–2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.

The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data

2016 ◽  
Author(s):  
Qiangqiang Xu ◽  
Zhengjun Liu ◽  
Fangfang Li ◽  
Mingze Yang ◽  
Haicheng Ren
Keyword(s):  
Change Detection ◽  
Multispectral Data ◽  
Object Based

scholarly journals The Relationship between the Mixed Pixel Spectral Value of Landsat 8 OLI Data and LAPAN Surveillance Aircraft (LSA) Aerial-Photo Data

2017 ◽  
Vol 31 (1) ◽  
pp. 83-98
Author(s):  
Nurwita Mustika Sari ◽  
Galdita Aruba Chulafak ◽  
Zylshal Zylshal ◽  
Dony Kushardono
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Actual Condition ◽  
Landsat 8 ◽  
Landsat Data ◽  
Aerial Photo ◽  
Visual Interpretation ◽  
Mixed Pixel ◽  
Multispectral Data ◽  
Medium Resolution

Medium resolution satellite data such as Landsat is very potential for mixed pixel (mixel) to occur. Indonesian land use diverse especially urban areas makes high potential mixel in the first Landsat pixel size of 30 meters x 30 meters on the actual condition. Aircraft multispectral aerial photo data LAPAN Surveillance Aircraft (LSA) with a spatial resolution reached 58 cm can display objects in more detail in these sizes. The purpose of this research is to study mixel on Landsat data with multispectral data LSA as a complement Landsat data. The method proposed in this study is a visual interpretation with GEOBIA method for classification of land cover, and then test the validity of the sample to be used in research, and the use of such vegetation index NDVI to see the connection between vegetation index data of vegetation index LSA with Landsat data. The results showed that the regression equation obtained by regression between NDVI of Landsat data and NDVI of  LSA with a significance of less than 0.05 is y = 0.732x - 0102 with a value of R2 = 0.887. Through these results we can conclude that the NDVI values on both the data related to one another.

scholarly journals CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 897-901 ◽  
Author(s):  
M. Tan ◽  
M. Hao
Keyword(s):  
Remote Sensing ◽  
Em Algorithm ◽  
Change Detection ◽  
Local Information ◽  
Experimental Results ◽  
False Alarms ◽  
Clustering Methods ◽  
Remote Sensing Images ◽  
Threshold Method ◽  
Medium Resolution

In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effectiveness of the proposed method.

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