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HY-2B, the second China's satellite for oceanic dynamic environment measurement, is scheduled to launch at 2018. To improve the capability, some new methods are adopted in HY-2B altimeter design. By these means, the long term stability of the instrument is improved compared with HY-2A altimeter. In this paper, the improved design based on a rubidium atomic clock for HY-2B altimeter is discussed in...
In Global Navigation Satellite System Radio Occultation (GRO), the tropospheric temperature and humidity can only be retrieved separately from refractivity by co-using a priori humidity or temperature information. Fortunately, the LEO-LEO (Low Earth Orbit) microwave occultation (LMO) exploits both the refraction and absorption of signals to solve the temperature-humidity ambiguity, and so it can retrieve...
The aim of the paper is to describe a ground-based microwave scatterometer demonstrator dedicated for the functional verification of MICAP (Microwave Imager Combined Active and Passive). MICAP has been selected to be one of the main payloads for future Chinese ocean salinity mission. The MICAP scatterometer is for estimating ocean surface roughness which measurement will enable the removal of the...
GF-4 is the first Chinese high resolution geostationary orbit satellite. It has great application potential in many earth-related studies. Given the low geometric accuracy of GF-4 images in mountain area, in this paper, a new operational and practical automatic orthorectification approach was proposed to improve the orthorectification accuracy of GF-4 images. The new approach adopted a two-level area-based...
This paper presents a rain rate retrieval algorithm for tropical cyclones (TCs) using passive microwave Fengyun-3B (FY-3B) MWRI brightness temperature (TB). Channel 18.7 and 36.5GHz of FY-3B/MWRI are used to implement retrieval algorithm. Two databases are constructed: one is 15,153 matchups of MWRI TB and NSIDC global swath ocean products for rain rate retrieving and another databset of 5,093 matchups...
This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the image classification itself. We present two strategies for multi-class classification problems based on the combination of GP-based indices defined in binary...
Ship classification with spaceborne high resolution synthetic aperture radar (SAR) has wide applications in maritime traffic monitoring, fishing law-enforcement operation, marine security, etc. Deep learning, which has the ability of learning features itself, is successfully used in computer vision and artificial intelligence, and introduced into remote sensing field in recent years. In this study,...
Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a data set by finding its meaningful low dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing data set, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective...
In the quest of developing more accurate methodologies for Earth Observation (EO) image retrieval, visualization and information content exploration, a deep understanding of the data being analyzed is needed. In this paper we propose a simple but efficient visual data mining methodology that can be used for these tasks. Our solution consists in a patch-based feature extraction to derive image features...
Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based...
Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
More information can be obtained with the improved spatial resolution of ultra-high resolution (UHR) SAR, whereas the increasing complexity and rich details lead to extreme difficulties in the automatic interpretation. In this paper, we propose a new texture feature set which involves four types of characteristic signatures and nine features to benefit applications of UHR SAR. Experiment based on...
Nonnegative matrix factorization (NMF) is often used for unsupervised spectral unmixing in recent years. In this paper, a constrained NMF algorithm based on the bilinear mixture models for unsupervised nonlinear spectral unmixing is proposed. By using a distance measure without dimension reduction, data's projection on a group of constructed hyperplanes representing the nonlinearity are obtained so...
Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue. Its idea is to use a Gaussian filter to capture...
Hyperspectral unmixing aims to estimate a set of endmembers and their corresponding percentages in pixels. NMF and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 regularizer and L2 regularizer can be added into NMF to enforce sparseness and smoothness respectively. In practice, an rigion in hyperspectral image may possesses different sparse level across...
In the field of target detection in remote sensing images, lots of learning algorithms have been presented, among which support vector machine was widely utilized. However, this kind of vector represents only one pixel of a remote sensing image that ignores the spatial relationship of neighbors. Besides, with the increase of spatial resolution of remote sensing images, detail detection of targets...
Sparse unmixing has been successfully applied to hyperspectral remote sensing imagery based on the assumption that the observed image signatures can be expressed in a linear sparse regression with a large standard spectral library. Prior work for sparse unmixing usually utilizes L1 norm or Laplacian distribution to promote sparsity. Unfortunately, the L1 norm is not differentiable, which may lead...
Mixed pixel is a ubiquitous phenomenon in remotely sensed imagery, especially in moderate and low spatial resolution imagery, which compromise the hard land cover classification since the dominant class will shadow the information of other vulnerable classes, bringing trouble to imagery interpretation. Since the past decades, sub-pixel mapping (SPM) approaches were developed to deal with the mixture...
The Doppler shift effect results in some targets shadows in theirs actual position, and a strong correlation exists between adjacent frames of Video Synthetic Aperture Radar (VideoSAR) imagery. Based on the above rationale, a novel approach to moving targets shadow detection for high-frame-rate VideoSAR imagery sequence is presented. First, a fast preprocessing stage is essential in real applications,...
In this research, we proposed the Synchro Water Index (SWI) to detect widespread inundation extent in a transboundary river basin using the time-series Moderate Resolution Imaging Spectrometer (MODIS) data, a major contributor to progress in international flood monitoring. After removing clouds using the White-object Index (WoI), the multi-temporal processing coupled with in-situ water level data...
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