Dynamic threshholds for land surface change detection using image differencing

2010 ◽  
Author(s):  
Sang-il Kim ◽  
Kyung-Soo Han ◽  
In-Hwan Kim ◽  
Jong-Min Yeom ◽  
Kyoung-Jin Pi
Keyword(s):  
Change Detection ◽  
Land Surface ◽  
Surface Change ◽  
Image Differencing

Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images

2001 ◽  
Vol 22 (13) ◽  
pp. 2463-2477 ◽  
Author(s):  
Jian Guo Liu ◽  
A. Black ◽  
H. Lee ◽  
H. Hanaizumi ◽  
J. McM. Moore
Keyword(s):  
Change Detection ◽  
Land Surface ◽  
Desert Area ◽  
Surface Change ◽  
Multi Temporal

Optimisation of the Uav-Ppk-Sfm Workflow for 4d Surface Change Detection in Steep Terrain

2021 ◽  
Author(s):  
Miluska Anthuannet Rosas ◽  
François Clapuyt ◽  
Willem Viveen ◽  
Veerle Vanacker
Keyword(s):  
Change Detection ◽  
Surface Change ◽  
Steep Terrain

Lunar Surface Change Detection with PyNAPLE: The 2017-09-27 Lunar Impact Flash and Impact Crater

2021 ◽  
Author(s):  
Daniel Sheward ◽  
Anthony Cook ◽  
Chrysa Avdellidou ◽  
Marco Delbo ◽  
Bruno Cantarella ◽  
...  
Keyword(s):  
Change Detection ◽  
Lunar Surface ◽  
Impact Crater ◽  
Surface Change ◽  
Lunar Impact ◽  
Impact Flash

Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

2012 ◽  
Vol 23 (2) ◽  
pp. 139-172
Author(s):  
Abdullah Salman Alsalman Abdullah Salman Alsalman
Keyword(s):  
Image Processing ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Test Area ◽  
Image Differencing ◽  
Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

scholarly journals Training Data Selection for Annual Land Cover Classification for the Land Change Monitoring, Assessment, and Projection (LCMAP) Initiative

2020 ◽  
Vol 12 (4) ◽  
pp. 699 ◽  
Author(s):  
Qiang Zhou ◽  
Heather Tollerud ◽  
Christopher Barber ◽  
Kelcy Smith ◽  
Daniel Zelenak
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Classification Accuracy ◽  
Training Data ◽  
Land Change ◽  
Auxiliary Data ◽  
Surface Change ◽  
Land Cover Map ◽  
The U.S ◽  
Change Monitoring

The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative involves detecting changes in land cover, use, and condition with the goal of producing land change information to improve the understanding of the Earth system and provide insights on the impacts of land surface change on society. The change detection method ingests all available high-quality data from the Landsat archive in a time series approach to identify the timing and location of land surface change. Annual thematic land cover maps are then produced by classifying time series models. In this paper, we describe the optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). The results were evaluated and validated based on independent data from the training dataset. We found that refining the auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability. We improved the classification accuracy and stability considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class within geographic windows consisting of nine Analysis Ready Data tiles (450 by 450 km2). Comparisons revealed that the NLCD 2001 training data delivered the best classification accuracy. Compared to the original LCMAP classification strategy used for early evaluation (e.g., Trends training data, 20,000 samples), the optimized classification strategy improved the annual land cover map accuracy by an average of 10%.

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