Synthetic aperture radar (SAR), with its all-weather and day/night capabilities, plays an important role in Earth observation. Traditionally, SAR satellites fly in low Earth orbits, which can result in fast Doppler accumulation but long revisiting intervals. Although increasing the number of satellites extends the observation coverage significantly, a complex constellation leads to unacceptable cost. The geosynchronous SAR system realizes sustained observations for a specific area; however, the long integral time results in the obvious defocusing of objects, even in micro-motion. To achieve a fast response and a short revisiting time with high accuracy and low costs, a constellation of imaging radar satellites called Constellation of Geostationary and Low Earth Orbit SAR (ConGaLSAR) is proposed in this letter; ConGaLSAR employs a novel transponding mode (MirrorSAR) to economically achieve efficient revisiting and phase/time synchronizations. In this system, the ground echoes are amplified by the low-orbit satellites and then retransmitted back to the illuminating system for sampling and downlinking. In view of the specific geometry of the system, the sensitivity, resolutions, and effective observing area are discussed and precisely defined. Based on the theoretical analyses, a typical case of ConGaLSAR, consisting of one geostationary orbit illuminator and 24 low-orbit transponders, can achieve a 92.4-min revisiting interval and 3-m resolution for the Pacific Ocean.

The traditional altimetry satellite, which is based on pulse-limited radar altimeter, only measures ocean surface heights along tracks; hence, leads to poorer accuracy in the east component of the vertical deflections compared to the north component, which in turn limits the final accuracy of the marine gravity field inversion. Wide-swath altimetry using radar interferometry can measure ocean surface heights in two dimensions and, thus, can be used to compute vertical deflections in an arbitrary direction with the same accuracy. This paper aims to investigate the impact of Interferometric Radar Altimeter (InRA) errors on gravity field inversion. The error propagation between gravity anomalies and InRA measurements is analyzed, and formulas of their relationship are given. By giving a group of possible InRA parameters, numerical simulations are conducted to analyze the accuracy of gravity anomaly inversion. The results show that the accuracy of the gravity anomalies is mainly influenced by the phase errors of InRA; and the errors of gravity anomalies have a linear approximation relationship with the phase errors. The results also show that the east component of the vertical deflections has almost the same accuracy as the north component.

Building detection from high-resolution synthetic aperture radar (SAR) image is an essential issue for many SAR applications in urban areas. In this letter, we propose a novel bottom-up/top-down hybrid algorithm for model-based building detection from single very high resolution (VHR) SAR image. First, the building model is generated and described by a set of extraction criteria, which restrict the spatial layout of a building and its primitive features. Specifically, the rectangles of different intensity levels are extracted from the SAR image as primitive features. Then the bottom-up stage proposes building candidates composed by extracted rectangles, and the top-down step predicts building candidates composed by weak features omitted in the primitive extraction. After that, all candidates are verified through false alarm detection. Under this framework, the detection performances can be greatly improved especially in dense built-up areas. The effectiveness of the proposed method is verified by experimental results obtained from real VHR SAR images.

Recently, the semisupervised learning for hyperspectral image classification (HIC) has attracted increasing attention. The previous semisupervised methods were usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we propose a novel Laplacian least squares support vector machine in sum space (Lap-LSSVM-SS) for semisupervised HIC with multiscale kernels, which can simultaneously deal with the high and low frequency components of the target classification function with small and large scale kernels, respectively. Furthermore, due to its representation theorem, a noniterative algorithm based on the closed-form classification function of Lap-LSSVM-SS is provided to make a rapid semisupervised HIC possible in the multiple kernel learning framework. The proposed method can solve the multiclass problem of hyperspectral dataset directly. We further reduce the storage and computation cost of the proposed Lap-LSSVM-SS method with the random sampling strategy, i.e., Lap-LSSVM-SS-RS. We also show that the Lap-LSSVM-SS-RS method has near-optimal classification performance when the number of local Lap-LSSVM-SS machines is not too large. The experimental results on two real hyperspectral datasets demonstrate that Lap-LSSVM-SS-RS yields superior classification performance over the conventional semisupervised classifiers (e.g., LapSVM, S4VM, and BagSVMs, etc.) in challenging small training labeled samples and a large amount of unlabeled samples.

Imaging Radar Altimeter (IRA) is the current development tendency for ocean surface topography (OST) altimetry, which utilizes Synthetic Aperture Radar (SAR) and interferometry to improve the spatial resolution of OST to several kilometers or even better. Meanwhile, centimetric altimetry accuracy should be guaranteed for applications such as geostrophic currents or marine gravity anomaly inversion. However, the baseline length of IRA which determines the altimetric sensitivity is confined by the satellite platform, in consideration of baseline vibration and payload capability. Therefore, the baseline length from a single satellite can extend to only tens of meters, making it difficult to achieve centimetric accuracy. Referring to the successful experience from TerraSAR-X/TanDEM-X, satellite formation can easily extend the baseline length to hundreds or thousands of meters, depending on the helix orbit. Therefore, we propose the large baseline IRA (LB-IRA) from satellite formation for OST altimetry: the carrier frequency shift (CFS) is brought in to compensate for the severe baseline decorrelation, and the helix orbit is carefully selected to prevent severe time decorrelation from along-track baseline. The numerical results indicate that the LB-IRA, whose cross-track baseline ranges between 629~1000 m and along-tack baseline ranges between 0~40 m, can achieve ~1 cm relative accuracy at 1 km resolution.

ConGalSAR is a novel SAR constellation that has an outstanding performance of revisit and economy. In the system, with the MirrorSAR technology, one geostationary illuminator and several economical low earth orbit transponders work together to achieve a short revisiting interval. Because of the huge difference in the distances of transmitting and receiving, the spatial weighting of the antenna patterns on the ground is quite different, which leads to the deterioration of ambiguity to signal ratio. The multiphase center and digital beamforming antennas can suppress the ambiguity energy; however, they are too expensive and heavy to be equipped on the dozens of transponding nanosatellites. In this article, a comprehensive ambiguity suppression method, including the reflector antenna design and signal processing, is proposed to reduce the ambiguity ratio, which well solves the ambiguity problem under the condition of the low manufacturing costs and lightweight transponders. In terms of the hardware design, the multifeed reflector antenna is applied to the transponder, in which the feeds receive an echo together. Based on this, the low sidelobes beamforming is achieved to well suppress the ambiguity. Meanwhile, the amplitude-modulated chirps are also used here aiming at the residual ambiguity in the large dynamic scene. Through spectral selection and extrapolation processing, range ambiguity energy is suppressed more while preserving the resolution. The digital simulations show that the comprehensive method can realize −20 dB ambiguity ratios at least, which conforms to the ambiguity-to-signal ratio design of ConGaLSAR.