This paper presents a novel approach for urban change detection of high resolution (HR) remote sensing images. To overcome deficiency of traditional pixel-based methods and better annotate HR images, object-based stra...
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ISBN:
(纸本)9781479979301
This paper presents a novel approach for urban change detection of high resolution (HR) remote sensing images. To overcome deficiency of traditional pixel-based methods and better annotate HR images, object-based strategies are adopted. Firstly change vector analysis (CVA) and local binary patterns (LBP) are utilized to extract the object-specific features based on the image-objects acquired by multitemporal segmentation. Then sparse representation is further exploited to characterize highly effective sparse features. Finally, the final change map is obtained by support vector machine (SVM) with the pseudotraining set acquired by expectation maximization (EM). Comparative experiments demonstrate the effectiveness of the proposed method.
Over the last two decades (since ca. 2000), Geographic object-based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interes...
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Over the last two decades (since ca. 2000), Geographic object-based Image Analysis (GEOBIA) has emerged as a new paradigm to analyzing high-spatial resolution remote-sensing imagery. During this time, research interests have demonstrated a shift from the development of GEOBIA theoretical foundations to advanced geo-object-based models and their implementation in a wide variety of real-world applications. We suggest that such a rapid GEOBIA evolution warrants the need for a systematic review that defines the recent developments in this field. Therefore, the main objective of this paper is to elucidate the emerging trends in GEOBIA and discuss potential opportunities for future development. The emerging trends were found in multiple subfields of GEOBIA, including data sources, image segmentation, object-based feature extraction, and geo-object-based modeling frameworks. It is our view that understanding the state-of-the-art in GEOBIA will further facilitate and support the study of geographic entities and phenomena at multiple scales with effective incorporation of semantics, informing high-quality project design, and improving geo-object-based model performance and results.
Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most ...
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ISBN:
(纸本)9780819492784
Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most striking artefacts of this transformation, threatening the local water supply systems and exacerbating water scarcity. In the face of climate change, urban landscape irrigation is becoming increasingly important from a resource management point of view. This paper adopts urban remote sensing towards a targeted mapping approach using machine learning techniques and high-resolution satellite imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Swimming pools, vegetation and - as a subgroup of vegetation - turf grass are extracted as important determinants of water consumption. For image analysis, the complex nature of urban environments suggests spatial-spectral classification, i.e. the complementary use of the spectral signature and spatial descriptors. Multiscale image segmentation provides means to extract the spatial descriptors - namely objectfeature layers - which can be concatenated at pixel level to the spectral signature. This study assesses the value of objectfeatures using different machine learning techniques and amounts of labeled information for learning. The results indicate the benefit of the spatial-spectral approach if combined with appropriate classifiers like tree-based ensembles or support vector machines, which can handle high dimensionality. Finally, a Random Forest classifier was chosen to deliver the classified input data for the estimation of evaporative water loss and net landscape irrigation requirements.
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