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Nuclear proliferation is a major national security concern for many countries. Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present an unsupervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze...
The constrained nonnegative matrix factorization algorithm (CNMF) has previously been shown to be a useful method to solve the unmixing problem in hyper spectral remote sensing images, but it also has some key weaknesses which affect its applied range. It's sensitive to the initial values, and easily falls to the local minimum. To solve the problems, a new intelligent optimization method - PSO(Particle...
Information of the urban road areas for resource management, security monitoring, urban development and Geographic Information System (GIS) is changing with the growing world. Satellite image provides useful data that is extracted from satellite image of the urban area. Automatic extraction of the road intersections from the urban areas has been a challenging topic because the high resolution satellite...
Classification is an important task in Hyperspectral data analysis. Hyperspectral images show strong correlations across spatial and spectral neighbors. Theoretically, classifier designed with a joint spectral and spatial correlations can improve classification performance than classifier which only utilize one of the correlations. Gaussian Processes(GPs) have been used for Hyperspectral imagery classification...
Multiplicative speckle noise is always present in synthetic aperture sonar (SAS) images, which is due to the coherent nature of scattering phenomena. Many methods that reduce speckle noise while preserving texture and detail have been developed for SAR and presented in the literature. In this paper, some speckle reduction techniques from SAR images are adapted and applied to SAS high resolution images...
To improve the precision of remote sensing image classification, hybrid multi-classifier combination method is proposed. Taking the characteristic of abstract level and measurement level into consideration, the optimal sub-classifier, bagging algorithm and the most large confidence algorithm are combined. By using this model, respective advantages of different sub-classifiers are gathered. This method...
Due to the proper structure and the nonuniformity of the imager detectors, the mosaic algorithms for SWIR (Short-Wave InfRared) remote sensing images slightly differentiate from common mosaic techniques. A detailed description of a novel mosaic method has been proposed in the paper. Firstly, pixels re-arranging and nonuniformity verifying have been done with the images during the pre-processing step...
Remote Sensing image registration plays an important role in image processing. The exact corner detection is vital to image registration. A modified SUSAN corner detection algorithm based on adaptive gradient threshold is presented. Ensuring the gray threshold automatically is the key of the new algorithm. First, the Traditional SUSAN principle and the new algorithm are discussed carefully. Then,...
An iterative algorithm which identifies the presence of different gases throughout a hyperspectral cube was developed and tested. The algorithm uses the “stepwise regression” method combined with new methods of detection and identification in the hyperspectral cube's analysis. This algorithm begins with a library of gas signatures; an initial fit is done with all the gases. The algorithm then eliminates...
This paper explains the task of interpreting any given satellite image by Genetically Optimized Hard C means(GOHCM). GOHCM has been used to segment the satellite image. Image segmentation is the process of dividing pixels into homogeneous classes or clusters so that items in the same cluster are as similar as possible and items in different cluster are as dissimilar as possible. The most basic attribute...
The growing use of hyperspectral imagery lead us to seek automated algorithms for extracting useful information about the scene. Recent work in sparse approximation has shown that unsupervised learning techniques can use example data to determine an efficient dictionary with few a priori assumptions. We apply this model to sample hyperspectral data and show that these techniques learn a dictionary...
Security has become an inseparable issue even in the field of space technology. Visual Cryptography is the study of mathematical techniques related aspects of Information Security which allows Visual information to be encrypted in such a way that their decryption can be performed by the human visual system, without any complex cryptographic algorithms. This technique represents the secret image by...
Super resolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft classification methods. Linear spectral unmixing have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. The use...
Remotely sensed hyperspectral imagery plays an important role in land cover classification by supplying the user with additional spectral data as compared to high-resolution color imagery. The web application described in this paper enables users to test their classification algorithms without the risk of bias by withholding the majority of the true classification data and only providing a small section...
Remotely sensed spectral imagery is used in many disciplines, including environmental monitoring, agricultural health, defense and security applications, astronomy, medical imaging, and food quality assessment. The basic tasks performed within any of these fields are target or anomaly detection, classification or clustering, change detection, and physical parameter estimation. Hyperspectral image...
This paper introduced a data embedding technology different from traditions. The study is based on visual C++ programming with interface. For convenience of users, It got over traditional defect of embedding technology. This study also produces new ideas in the aspect of precise correction. A more precise TIN-based method is assumed. Using this method, the errors of the corrected image is about one...
A satellite precipitation estimation algorithm based on wavelet features is investigated to find the optimal wavelet features in terms of wavelet family and sliding window size. In this work, the infrared satellite based images along with ground gauge (radar corrected) observations are used for the retrieval rainfall. The goal of this work is to find an optimal wavelet transform to represent better...
Human settlement regions with different physical and socio-economic attributes exhibit unique spatial characteristics that are often illustrated in high-resolution overhead imageries. For example-size, shape and spatial arrangements of man-made structures are key attributes that vary with respect to the socio-economic profile of the neighborhood. Successfully modeling these attributes is crucial in...
An attempt has been made in the paper to find globally optimal cluster centers for remote-sensed images with the proposed Rapid Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in the problem is the usage of an expensive crossover or fitness to produce valid clusters...
Vegetation includes all plant communities that cover the earth surface. Vegetation coverage is the most important index to scale the status of the vegetation of the region, so the vegetation is the composition and the function body of ecological system. Vegetation coverage in different land use types of Miyun reservoir watershed were calculated based on remote sensing images (Landsat 5 TM images)...
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