Urbanization analysis by mutual information based change detection between SPOT 5 panchromatic images

2011 ◽  
Author(s):  
Lionel Gueguen ◽  
Martino Pesaresi ◽  
Daniele Ehrlich ◽  
Linlin Lu
Keyword(s):  
Mutual Information ◽  
Change Detection ◽  
Spot 5

A Comparison of Two Object-Oriented Methods for Land-Use/Cover Change Detection with SPOT 5 Imagery

2012 ◽  
Vol 10 (1) ◽  
pp. 415-424 ◽  
Author(s):  
Jie Liang ◽  
Jianyu Yang ◽  
Chao Zhang ◽  
Xuejiao Du ◽  
Anzhi Yue ◽  
...  
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Object Oriented ◽  
Object Oriented Methods ◽  
Spot 5

scholarly journals VOLUMETRIC FOREST CHANGE DETECTION THROUGH VHR SATELLITE IMAGERY

2016 ◽  
Vol XLI-B8 ◽  
pp. 1213-1220
Author(s):  
Devrim Akca ◽  
Efstratios Stylianidis ◽  
Konstantinos Smagas ◽  
Martin Hofer ◽  
Daniela Poli ◽  
...  
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Satellite Imagery ◽  
Optical Sensors ◽  
Optical Data ◽  
Surface Matching ◽  
Surface Model ◽  
3D Surface ◽  
Optical Imagery ◽  
Spot 5

Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView – 3, SPOT – 5 HRS, SPOT – 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in six case studies located in Austria, Cyprus, Spain, Switzerland and Turkey, using optical data from different sensors and with the purpose to monitor forest with different geometric characteristics. The validation run on Cyprus dataset is reported and commented.

Multicontextual Mutual Information Data for SAR Image Change Detection

2015 ◽  
Vol 12 (9) ◽  
pp. 1863-1867 ◽  
Author(s):  
Lin An ◽  
Ming Li ◽  
Peng Zhang ◽  
Yan Wu ◽  
Lu Jia ◽  
...  
Keyword(s):  
Mutual Information ◽  
Change Detection ◽  
Sar Image ◽  
Image Change Detection

scholarly journals Evaluation of spectral similarity indices in unsupervised change detection approaches

DYNA ◽  
2018 ◽  
Vol 85 (204) ◽  
pp. 117-126 ◽  
Author(s):  
Jeisson Fabian Ramos ◽  
Diego Renza ◽  
Dora M. Ballesteros L.
Keyword(s):  
Change Detection ◽  
Spectral Similarity ◽  
Similarity Indices ◽  
Spot 5

La detección de cambios de forma no-supervisada (UCD) es un área de teledetección, cuyo objetivo consiste en encontrar las diferencias entre dos imágenes multi-temporales. En algunos casos, los índices de similitud espectral son utilizados como bloque de comparación de UCD. El objetivo de este documento consiste en analizar de forma cuantitativa el desempeño de cuatro índices de similitud espectral en la correcta identificación de cambios. La evaluación se realiza en términos de la precisión (mediante la precisión global e índice kappa) utilizando imágenes de media y alta resolución (SPOT-5: Satélite Para la Observación de la Tierra y Quickbird), así como una imagen de cambio de referencia obtenida a través de un método de post-clasificación (basado en Máquinas de Soporte Vectorial, SVM). Los resultados obtenidos presentan dependencia con la técnica automática de umbralización, así como con las clases asociadas con el cambio.

A Multi-Level Approach for Change Detection of Buildings Using Satellite Imagery

2018 ◽  
Vol 27 (08) ◽  
pp. 1850031 ◽  
Author(s):  
Md. Abdul Alim Sheikh ◽  
Alok Kole ◽  
Tanmoy Maity
Keyword(s):  
Remote Sensing ◽  
Mutual Information ◽  
Change Detection ◽  
Satellite Imagery ◽  
Image Matching ◽  
Google Earth ◽  
Remote Sensing Images ◽  
Normalized Mutual Information ◽  
Novel Technique ◽  
Multi Level

In this paper a novel technique for building change detection from remote sensing imagery is presented. It includes two main stages: (1) Object-specific discriminative features are extracted using Morphological Building Index (MBI) to automatically detect the existence of buildings in remote sensing images. (2) Pixel-based image matching is measured on the basis of Mutual Information (MI) of the images by Normalized Mutual Information (NMI). Here, the MBI features values are computed for each of the pair images taken over the same region at two different times and then changes in these two MBI images are measured to indicate the building change. MI is estimated locally for all the pixels for image matching and then thresholding is applied for eliminating those pixels which are responsible for strong similarity. Finally, after getting the MBI and NMI images, a further fusion of these two images is done for refinement of the change result. For evaluation purpose, the experiments are carried on QuickBird, IKONOS images and images taken from Google Earth. The results show that the proposed technique can attain acceptable correctness rates above 90% with Overall Accuracy (OA) 89.52%.

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