scholarly journals Which principal components are most sensitive in the change detection problem?

Stat ◽  
2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Tveten
Keyword(s):  
Change Detection ◽  
Principal Components ◽  
Detection Problem

Change detection in hyperspectral imagery using temporal principal components

2006 ◽  
Author(s):  
Vanessa Ortiz-Rivera ◽  
Miguel Vélez-Reyes ◽  
Badrinath Roysam
Keyword(s):  
Change Detection ◽  
Principal Components ◽  
Hyperspectral Imagery

Land Cover and Land Use: Classification and Change Analysis

2004 ◽  
Author(s):  
Rinku Roy Chowdhury ◽  
Laura C. Schneider
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Pattern ◽  
Principal Components ◽  
Secondary Forest ◽  
Landsat Tm ◽  
Change Analysis ◽  
Deforestation Rates ◽  
Cover Class

Despite its international designation as a hotspot of biodiversity and tropical deforestation (Achard et al. 1988), the micro-scale land-cover mapping of southern Yucatán peninsular region remains surprisingly incomplete, hindering various kinds of research, including that proposed in the SYPR project. This chapter details the methodology for the thematic classification and change detection of land use and cover in the tropical sub-humid environment of the region. A hybrid approach using principal components and texture analyses of Landsat TM data enabled the distinction of land-cover classes at the local scale, including mature and secondary forest, savannas, and cropland/pasture. Results indicate that texture analysis increases the statistical separability of cover class signatures, the magnitude of improvement varying among pairs of land-cover classes. At a local level, the availability of exhaustive training site data over recent history (10–13 years) in a repository of highly detailed land-use sketch maps allows the distinction of greater numbers of land-cover classes, including three successional stages of vegetation. At the regional scale, finely detailed land-cover classes are aggregated for greater ability to generalize in a terrain wherein vegetation exhibits marked regional and seasonal variation in intra-class spectral properties. Post-classification change detection identifies the quantities and spatial pattern of major land-cover changes in a ten-year period in the region. Change analysis results indicate an average annual rate of deforestation of 0.4 per cent, with much regional variation and most change located at three subregional hotspots. Deforestation as well as successional regrowth is highest in a southern hotspot located in the newly colonized southern part of the region, an area where commercial chili production is large. The objectives of this chapter are to describe and evaluate: (1) an experimental methodology that iteratively combines three suites of image-processing techniques (PCA, texture transformation, and NDVI); (2) the statistical separability of distinct land-cover signatures; and (3) a post-classification change detection for the region from 1987 to 1997 in order to derive regional deforestation rates, and identify the spatial pattern of deforestation and secondary forest succession. Specifically, a region encompassing 18,700km2 (those land units completely within the defined region; Fig. 7.1) was mapped using a maximum likelihood supervised classification of lower-order principal components of Landsat TM imagery after tasseled-cap and texture transformations.

scholarly journals MAPPING EXTENSION AND MAGNITUDE OF CHANGES INDUCED BY CYCLONE IDAI WITH MULTI-TEMPORAL LANDSAT AND SAR IMAGES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 273-277
Author(s):  
C. Amisse ◽  
M. E. Jijon-Palma ◽  
J. A. S. Centeno
Keyword(s):  
Image Segmentation ◽  
Change Detection ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sar Images ◽  
Detection Techniques ◽  
Multi Temporal ◽  
Components Analysis

Abstract. In this paper it is described a study case of a rapid assessment of change detections for post-cyclone Idai vegetated damage and flood extension estimation by fusion of multi-temporal Landsat and sentinel-1 SAR images. For automated change detection, after disasters, many algorithms have been proposed. To visualize the changes induced by cyclone we tested and compared two automated change detection techniques namely: Principal Components Analysis (PCA), Normalized Difference Vegetation Index (NDVI) and image segmentation. With the image segmentation of multispectral and SAR images, it was possible to visualize the extension of the wet area. For this specific application, PCA was identified as the optimal change detection indicator than NDVI. This study suggested that image segmentation, principal components analysis, and normalized difference vegetation index can be used for change detection of surface water due to flood and disasters especially in prone countries like Mozambique.

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