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The parcel-based changed detection by adopting the holistic feature can extract the changed parcels in land-use maps[1]. This method of parcel-based change detection uses the holistic feature, which represents each land use parcels clipped by polygons in the land use map with the energy spectrum of WFT and extracts the changed parcels according to the distance threshold between feature vectors associated...
Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as...
Remote sensing has been widely applied for environmental monitoring by means of change detection techniques, commonly for identifying deforestation signs which is the gateway for illegal activities such as uncontrolled urban growth and grazing pasture. Monthly acquired X-Band images from airborne Synthetic Aperture Radar (SAR) provided multi-temporal scenes employed in this work resulting in environmental...
Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematically...
In this paper, an unsupervised change detection model based on hybrid conditional random field model (HCRF) is proposed for high spatial resolution (HSR) remote sensing imagery. Traditional random field based algorithms are mainly based on the analysis of the difference image which ignores the spatial-temporal change information of ground objects which is important in dealing with HSR imagery. Thus...
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