Shadow Detection and Reconstruction in Satellite Images using Support Vector Machine and Image In-painting

ABSTRACT: In this paper an approach for the detection of shadows in satellite images using Support Vector Machine is proposed. The first step is to classify the shadow and non-shadow regions with the help of Support Vector Machine. In order to remove the noise in the classified image median filter is used. The reconstruction of the shadow areas is done by using image in-painting technique. This technique is used to retain the missing parts in an image due to shadows. The performances are evaluated by means of Peak Signal-to-Noise Ratio (PSNR), and Mean Square Error (MSE)

Shadow Detection from VHR Images using Clustering and Classification

This project mainly focus to get the high resolution color remote sensing image, and also undertaken to remove the shaded region in the both urban and rural area. Some of the existing projects are involved to detect the shaded region and then eliminate that region, but it has some drawbacks. The detection of the edges will be affected mostly by the application of the external parameters. The edge detection process can be more helpful in the detection of the objects so that the objects can be used for further processing. In this process we have implement the Scale Space algorithm is used to detect the shadow region and extract the feature from the shadow region. Scale Space is simplest in region-base image segmentation methods. The concept of Scale Space algorithm is check the neighboring pixels of the initial seed points. Then determine whether those neighboring pixels are added to the seed points or not. In the Scale Space threshold algorithm Pixels are placed in the region based on their properties or the properties of the nearby pixel values. Then the pixel containing the similar properties is grouped together and then the large numbers of pixels are distributed throughout the image

Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images

This task mostly center to get the high resolution color remote sensing image, and furthermore attempted to eliminate the concealed district in the both metropolitan and country region. A portion of the current activities are included to recognize the concealed district and afterward dispense with that area, yet it has a few disadvantages. The discovery of the edges will be influenced generally by the utilization of the outside boundaries. The edge location cycle can be more useful in the recognition of the articles with the goal that the items can be utilized for additional handling. In this cycle we have execute the Scale Space algorithm is utilized to identify the shadow area and concentrate the component from the shadow district. Scale Space is least complex in area base image segmentation strategies. The idea of Scale Space algorithm is check the neighboring pixels of the underlying seed focuses. At that point decide if those neighboring pixels are added to the seed focuses or not. In the Scale Space limit algorithm Pixels are set in the area dependent on their properties or the properties of the close by pixel esteems. At that point the pixel containing the comparable properties is gathered and afterward the enormous quantities of pixels are circulated all through the image

An assessment of shadow enhanced urban remote sensing imagery of a complex city - Hong Kong

Author name used in this manuscript: Bruce A. KingRefereed conference paper2012-2013 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

A SHALLOW NEURAL NETWORK MODEL FOR URBAN LAND COVER CLASSIFICATION USING VHR SATELLITE IMAGE FEATURES

Recently, image classification techniques using neural networks have received considerable attention in sustainable urban development, since their applications have an extreme effect on building distribution, infrastructural networks, and water resource management. In this research, a back-propagation shallow neural network model is presented for very high resolution satellite image classification in urban environments. Workflow procedures consider selecting and collecting data, preparing required study areas, extracting distinctive features, and applying the classification process. Visual interpretation is performed to identify observed land cover classes and detect distinctive features in the urban environment. Pre-processing techniques are implemented to present the used images in a more suited form for the classification techniques. A shallow neural network model (supported by MathWorks MATLAB environment) is successfully applied and results are evaluated. The proposed model is tested for classifying both WorldView-2 and WorldView-3 multispectral images with different spatial and spectral characteristics to check the model’s applicability to various kinds of satellite imagery and different study areas. Model outcomes are compared to two well-known classification methods; the Nearest Neighbour object-based method and the Maximum Likelihood pixel-based classifier, to validate and check the model stability. The overall accuracy achieved by the proposed model is 86.25% and 83.25%, while the nearest neighbour approach has obtained 84.50% and 82.75%, and the maximum likelihood classifier has accomplished 82.50% and 80.25% for study area 1 and study area 2 respectively. Obtained results indicate that the developed shallow neural network model achieves a promising accuracy for urban land cover classification in comparison with the standard techniques

Semiautomatic extraction strategy of urban road junctions from high resolution orthoimages

The aim of this paper is present and asses a strategy for semi-automatic extraction of road junctions from high resolution orthoimages take over urban areas. The proposed strategy is performed by collecting a set of samples data training and generates decision tree containing the explicit knowledge of the object classes, by WEKA software. On the roads defined, are applied morphological operators and algorithms that allow the detection of skeletons, junctions, selection of reference points and the roads junction extraction. The proposed strategy was tested with real data and the results were analyzed, allowing an evaluation of the strategy, as well as potentially problematic situations. The results presented in the experiments demonstrated the feasibility of road extraction in urban area scenes, road networks formed by simple and complex intersections. Neste trabalho é apresentada e avaliada uma estratégia semiautomática para a extração do cruzamento de vias com o uso de ortoimagens de alta resolução espacial em cenas correspondentes a áreas urbanas densas. Na estratégia proposta a extração da malha viária é realizada através da coleta de um conjunto de dados de treinamento de amostras que gera, no software WEKA, a árvore de decisão contendo o conhecimento explícito das classes presentes nas imagens. Sobre a malha viária definida, são aplicados operadores morfológicos e algoritmos que permitem a geração de eixos de vias (esqueletos), detecção de hipóteses de cruzamentos, seleção de pontos de referência e a extração do cruzamento de vias. A estratégia propostas foi testada com dados reais e os resultados obtidos foram analisados, permitindo uma avaliação da estratégia, bem como das situações potencialmente problemáticas. Os resultados apresentados nos experimentos demonstraram a viabilidade da extração de vias em cenas de áreas urbanas densas, formadas por malhas viárias com cruzamentos simples e complexos