For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

The degree of reception of BS signals is affected by various factors. After routinely recording it at two observation points at two locations, we found that momentary upward and downward level shifts occurred multiple times, mainly during daytime. These level shifts were observed at one location. No such signal was sensed at the other location. After producing an algorithm to extract such momemtary level shifts, their statistical properties were investigated. Careful analyses, including assessment of the signal polarity, amplitude, duration, hours, and comparison with actual flight schedules and route information implied that these level shifts are attributable to the interference of direct and reflected waves from aircraft flying at approximately tropopause altitude. This assumption is further validated through computer simulations of BS signal interference.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

Object detection has been a hot topic of image processing, computer vision and pattern recognition. In recent years, training a model from labeled images using machine learning technique becomes popular. However, the relationship between training samples is usually ignored by existing approaches. To address this problem, a novel approach is proposed, which trains Siamese convolutional neural network on feature pairs and finely tunes the network driven by a small amount of training samples. Since the proposed method considers not only the discriminative information between objects and background, but also the relationship between intraclass features, it outperforms the state-of-arts on real images.

This paper presents an experimental evaluation of an ocean wave remote sensing system that uses bistatic GPS signal reflection to estimate wave characteristics. In our previous paper, a bistatic ocean wave remote sensing system by GPS was proposed to estimate the characteristics of sea swell near a harbor, and was also evaluated by numerical simulations. In the next phase, a prototype system has been developed and some basic experiments have been carried out in a coastal area in order to evaluate the system experimentally. In this paper, we will outline the prototype system. The system mainly consists of an array antenna, a front-end, and an estimator for ocean wave characteristics. Next, we explain that the estimator for ocean wave characteristics can identify each signal reflected from the ocean waves. Finally, the experiments show that the prototype system can receive the reflected signals from the sea-surface near the coast, and estimate the wave period and wavelength in the direction of the array antenna.

A method for the specification of weighting functions for a spaceborne/airborne interferometric synthetic aperture radar (SAR) sensor for Earth observation and environment monitoring is introduced. This method is based on designing an optimum mismatched filter which minimizes the total power in sidelobes located out of a specified range region around the peak value point of the system point-target response, i.e. impulse response function under the constraint imposed on the peak value. It is shown that this method allows achieving appreciable improvement in accuracy performance without degradation in the range resolution.

In the work we introduce novel approach to remote sensing from space for the estimation of sea wave heights with a spaceborne high precision two-frequency radar altimeter with nadir synthesis antenna aperture. Experiments show considerable reduction of the decorrelation factor of the correlation coefficient and so significant enhancement of the sensitivity of the altimeter for the estimation for the sea wave status.

This paper addresses the ambiguity of radar altimetry related to the statistical nature of the Earth's surface roughness. A hypothetic altimetry method, which provides a simple way to quantify the measures of ambiguity, is proposed. Cramer-Rao lower bounds on the variances of estimates for the mean altitude and root-mean-square height of the sea surface are suggested as such quantitative measures. The accuracy of some types of air- and spaceborne radar altimeters is numerically analyzed against the derived lower bounds.

This paper analyses the autocorrelation function of return waveforms in high precision radar altimeters employing chirp-pulse transmit signal under the condition of near-nadir deviations of the antenna boresight axis. It is shown that in case of ultra wideband transmit signals providing very high time resolution the correlation function can be approximated by a product of two separate functions of time.

In this letter, a new formula is proposed for calculating the polarization entropy, based on the least square method. There is no need to calculate the eigenvalues of a covariance matrix as well as to use logarithms of values. So the time for computing the polarization entropy is reduced. Using polarimetric SAR data, the authors validate the effectiveness of the new formula.

This paper describes the contours of a Dutch monitoring and research site for climate change and related atmospheric processes. The station has large benefits for atmospheric science, both in The Netherlands and internationally. It provides a platform for collaboration in this important field, and will provide the routine observations needed to assess the impact of the different atmospheric parameters on the local climate. The station fits in directly in the selected group of global monitoring networks that are currently operational or being set up to address the problems of climate. In addition, the station can play a major role in supporting worldwide satellite measurements of climate related parameters. The only way to get a global picture of the essential climate change parameters can be found in the combination of satellite measurements and ground-based stations equipped with advanced remote sensing and in situ instrumentation. Furthermore, the combined expertise of European universities and research institutes, encompassing the whole field of atmospheric research, offers a unique chance for the training of young scientists. The research site is an attractive center for international young scientists to develop and deepen their skills.

In this letter, the concept of cross-entropy is introduced for unsupervised polarimetric synthetic aperture radar (SAR) image classification. The difference between two scatterers is decomposed into three parts, i.e., the difference of average scattering characteristic, the difference of scattering randomness and the difference of scattering matrix span. All these three parts are expressed in cross-entropy formats. The minimum cross-entropy principle is adopted to make classification decision. It works well in unsupervised terrain classification with a NASA/JPL AIRSAR image.

A pilot needs operational information about wind over sea as well as wave height to provide safety of hydroplane landing on water. Near-surface wind speed and direction can be obtained with an airborne microwave scatterometer, a radar designed for measuring the scatter characteristics of a surface. Mostly narrow-beam antennas are applied for such wind measurement. Unfortunately, a microwave narrow-beam antenna has considerable size that hampers its placement on flying apparatus. In this connection, a possibility to apply a conventional airborne radar altimeter as a scatterometer with a nadir-looking wide-beam antenna in conjunction with simultaneous range Doppler discrimination techniques for recovering the wind vector over sea at low speed of flight is discussed, and measuring algorithms of sea surface wind speed and direction are proposed. The principle considered and algorithms proposed in the paper can be used for creation an airborne radar system for operational measurement of the sea roughness characteristics and for safe landing of a hydroplane on water.

In this paper we present reconfigurable computing as a compelling choice of computing platform for real-time, onboard processing for satellite applications. In particular, we discuss the use of reconfigurable computing in the context of a real-time remote sensing system, providing motivation for such a system and describing attributes of reconfigurable computing that support it as the technology of choice. The High Performance Computing (HPC-I) payload, designed and developed for the Australian scientific satellite FedSat, is introduced as a demonstration of onboard processing in space using reconfigurable logic. We present an overview of the real-time remote sensing system architecture, and describe the design and implementation of three remote sensing algorithms in HPC-I for cloud masking, wildfire detection and volcanic plume detection. Finally, results from simulation and testing are presented which show very promising performance in terms of data throughput and detection capabilities.

The concept of the equi-phase curve is introduced for the cross-polarized channel case. It is proved that the equi-phase curves are a series of half circles on the Poincare sphere, and that all these curves have two common ends. Based on the introduced concept, this letter demonstrates the distribution of the received voltage's phases on the Poincare sphere. In addition, it is shown theoretically that the cross-polarized phase of the off-diagonal elements of a scattering matrix is unstable for most natural targets. Therefore, the cross-polarized phase information cannot be used for extracting target characteristics in polarimetric radar remote sensing.

The scatterometer is a real aperture radar capable to perform a set of normalized radar cross section measurements under different azimuth angles for each resolution cell. The main field of application of a wind scatterometer regards the sea surface wind field determination. As a matter of fact, once such measurements have been performed it is possible to determine the sea surface wind field by means of an inversion procedure. In this paper we present a novel inversion scheme which is an evolution of the procedure nowday used by the Italian Space Agency (ASI) under the Italian Processing and Archiving Facility (I-PAF). A full comparative study shows that the novel inversion scheme better behaves whenever light wind regimes are in question.

This paper presents methods for estimating the ocean wavelength in satellite altimetry and discusses the possibility of detecting the ocean wavelength. Numerical analyses show that there exists a relation between the significant wavelength and the pulse-to-pulse correlation coefficient. Presented methods are based on processing the data of existing satellite altimeters. Accordingly, we will be able to propose a method for measuring the wavelength without a large change in existing altimetry system.

The VHF broadband radio interferometry operated from 25 MHz to 250 MHz has been developed for observing lightning discharge progression. The lightning images are derived by sensing the electromagnetic-waves which are radiated during the discharges. The perpendicular baseline geometry provides the angular locations (azimuth and elevation) of lightning radiation sources. The lightning observations have been conducted in the Hokuriku District in 1999. The station consisted of three broadband antennas and an electric field antenna as well as a GPS receiver. The system was able to reconstruct lightning discharge channels in two-spatial dimensions and in time sequence. As one of the observation results, an upward negative cloud-to-ground lightning flash will be presented.

This paper describes an individual effect of soil moisture (mg) and surface roughness (hrms) of bare soil on the back scattering coefficient (σ0) at the X-band frequency. The study contributes to the design of an efficient microwave sensor. For this purpose, experimentally observed data was utilized to provide a composite σ0 equation model accounting for individual effect in regression analysis. The experimental data are compared with Small Perturbation Method. It is observed that the X-band gives better agreement up to incidence angle 50 for HH-polarization and 60 for VV-polarization as compared to the C-band. The lower angles of incidence give better results than the higher angles for observing mg at the X-band. The multiple and partial regression analyses have also carried out for predicting the dependence of scattering coefficient (σ0) on mg and hrms more accurately. The analyses suggest that the dependence of dielectric constant (i.e., mg) is much more significant in comparison to surface roughness at lower angles of incidence for both like polarizations. The results propose the suitable angle of incidence for observing bare surface roughness and soil moisture at the X-band. All these data can be used as a reference for satellite or spaceborne sensors.