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A combined L-band active and passive sensor for space-based ocean salinity observation has been proposed in MIRSlab, CAS [1] [2]. A ground-based prototype has been developed to demonstrate the concept and performance of the instrument. The experimental result of the passive part of the prototype, the L-band 1-D synthetic aperture radiometer, has been introduced in this paper.
Microwave interferometric radiometer (MIR) is a passive sensor using synthetic aperture technique for microwave remote sensing. Antenna array configuration is the first concern for the MIR application. Various array configurations have been proposed. This paper presents the analysis of the circular array performance and compares it with the Y-shaped array and hexagonal array. Based on the early developed...
Imaging period is an important consideration to geostationary interferometric microwave sounder (GIMS) when mapping fast changing target such as typhoon. GIMS simulation system with near real case observation target can evaluate system performance in different system configurations and thus help determine the optimal imaging period. In this paper, GIMS simulation system using MATLAB and near real...
Based on sparsity or compressibility of microwave radiation images, Compressive Sensing in this paper is adopted to achieve microwave radiation imaging in order to reduce the complexity and hardware cost of the imaging system and get the image of high spatial resolution. Compared with the common wavelet basis, differential matrix is proposed to sparsely represent microwave radiation images. OMP algorithm...
Full Polarization Interferometric Radiometer (FPIR) is a one-dimensional full polarization Interferometric radiometer for the Sea Surface Salinity (SSS). A ground-based prototype has been developed to demonstrate the concept and performance of the instrument. To achieve the SSS measurement accuracy, a new calibration system has been designed for FPIR. And a new calibration procedure has been developed...
Built-up areas are typical man-made structure in urban environment, and timely and accurately acquiring built-up area layers can provide necessary geo-spatial information for planners and policymakers. In this paper, a data field-based method is proposed for the automated detection of built-up areas from high-resolution satellite images. This method views the local corner features of buildings as...
Recently airborne SAR at very high frequencies, such as X- and Ku-bands, has been widely studied for many applications. In these studies, the backscatter properties at different frequencies have been well discussed. However, these properties of the urban objects and structures have not been sufficiently discussed yet. In this paper, we focus on evaluating the difference of Ku- and X-band SAR intensity...
This paper introduces a novel multi-layer line grouping method for perceptually building extraction from stereo aerial images. Nowadays, perceptual grouping algorithm for line features obtained from images has been widely investigated, but there are little attentions to be paid to building height information of the line segments applied in existing literature of edge grouping field. In order to enhance...
Cloud removal is significantly needed for enhancing the further utilization of Landsat imagery, since such optical remote sensing satellite images are inevitably contaminated by clouds. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. Generally, pixel replacement is the only and common method used to remove thick opaque clouds, and radiometric...
With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false...
An algorithm for automatic recognition of overwater bridge target based on “joint feather and knowledge rule-based” is presented for the problem concerning automatic recognition of overwater bridge target in optical remote sensing images. Firstly, based on knowledge feathers of overwater bridge target, waters in an optical remote sensing image are extracted to narrow down bridge detection range. After...
In this paper, a sub-pixel mapping (SPM) method based on super-resolution then spectral unmixing (SRTSUSPM) is proposed. In the proposed framework, firstly projection onto convex set (POCS) model with the endmembers of interest is applied to original imagery to obtain a high-resolution imagery; then the fraction images are derived from the high-resolution imagery by linear spectral mixture analysis...
Local binary patterns (LBP) features extracted from hyperspectral imagery (HSI) have gained impressive performance in hyperspectral classification tasks, for which LBP got considerable attention. However, existing LBP-based hyperspectral imagery classification methods utilized two-dimensional LBP (2DLBP) that could capture gray variation signal in space, which did not excavate the contextual information...
In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that...
In this paper, a spectral-spatial classification method with Gaussian process was proposed for hyperspectral image classification. This method exploits the relationship among adjacent pixels and integrates it into spectral information to obtain spectral-spatial classification. In the proposed approach, the spatial information of a single pixel is weighted by the cosine similarity value between the...
In this paper, a novel kernel low rank representation (KLRR) method for hyperspectral image classification is proposed. Firstly, we extract the global structure characteristics information of the hyperspectral image based on low rank representation (LRR), then use it as a prior to constrain the recovery coefficient matrix. In order to further improve the classification efficiency and deal with the...
This paper presents a novel method for hyperspectral classification combining multiple features and exploiting spatial information at the same time. We proposed a supervised classification method under the Markov random field (MRF)-based framework. Firstly using the probability SVM to map multiple features from different low-level subspace to the same semantic space (probability space), then integrating...
Support vector machines is a very popular method in classification of hyperspectral images due to their good generalization capability even with a limited number of training datasets. However, the performance of SVM strongly depends on selection of kernel parameters when RBF kernel is used. In order to achieve a high classification performance, the kernel parameters, that are the value of regularization...
Nowadays, hyperspectral images have been an attractive subject for many researches in remote sensing area since they provide abundant information due to their wide range of spectral bands. On the one hand, classification plays a significant role in extraction of information for different applications. On the other hand, providing a huge amount of data by hyperspectral images may lead to complexity...
This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be...
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