首页期刊导航|IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing
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IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing
Institute of Electrical and Electronics Engineers
Institute of Electrical and Electronics Engineers
双月刊
0196-2892
IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing/Journal IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensingSCIISTPEIAHCI
查看更多>>摘要：Temperature and emissivity separation (TES) is a crucial process for converting thermal infrared (TIR) hyperspectral data into actionable information. Since the development of thermal infrared hyperspectral imagers is still in its nascent stage, most TES algorithms have been validated primarily using simulated data within the context of land resource remote sensing. However, there has been insufficient focus on urban environments with complex underlying surfaces, particularly on low emissivity targets. In this study, we developed a TES algorithm capable of adapting to a broader emissivity range. The performance of the algorithm in retrieving temperature and emissivity was evaluated against several typical TES algorithms, using data cubes acquired by the airborne thermal infrared hyperspectral imaging system (ATHIS) as test data. The experimental results revealed that existing algorithms exhibited relatively high retrieval errors for low emissivity ground objects. In contrast, the developed algorithm significantly improved the accuracy of retrieval for such objects while maintaining comparable accuracy for non-low-emissivity targets. These findings suggest that the proposed algorithm enhances TES accuracy and expands the applicability of thermal infrared hyperspectral imaging in environmental remote sensing of urban environments with complex underlying surfaces.
查看更多>>摘要：Synthetic aperture radar (SAR) altimeter is able to measure height with high precision, which has been extensively applied to airborne aircrafts for positioning. With the assumption of a flat surface following a Gaussian distribution, the height is obtained by retracking the waveform. However, this assumption often fails in a complex terrain, leading to unpredictable height bias. In this article, a slope-based height correction (SHC) toward robust height inversion in complex terrain is proposed using linear terrain decomposition. Based on the radar propagation equation, the impact of the topography on height inversion is investigated. Then, the response from complex topography can be divided into a determined part related to a set of primary slopes and a stochastic part with certain undulation. Additionally, the height bias induced by the slopes is given based on power constraints. The error bound of the height correction is also derived correspondingly. The main advantage of the proposed method is reliable height correction with the prior digital elevation model (DEM). Experimental results based on both simulated and real data demonstrate a significant decrease in height bias, which greatly extends the application of the altimeter.
查看更多>>摘要：Signals emitted by ground-based sources frequently interfere with those received by spaceborne synthetic aperture radar (SAR) systems, with periodic pulses from ground radars being the most common form of interference. This article introduces a method to locate the source of these pulsed signals utilizing SAR echo data. Initially, the time difference of arrival (TDOA) for each pulse transmitted by the interferer and received by the SAR satellite is estimated. A mapping relationship between the interferer coordinates (latitude and longitude) and the variations in TDOA is then established. Using this mapping, the localization of the interferer is finally performed via a 2-D search approach. The proposed method is highly versatile and applicable to systems consisting of one single SAR, constellations of SAR instruments, and configurations of multichannel single-instrument SAR systems. Regardless of the modulation form of the interfering signal, the method remains effective. The accuracy of the method is analyzed and the factors influencing the localization precision are investigated. Finally, using the Gaofen-3 spaceborne SAR, an experimental validation of the proposed TDOA-based localization method is performed. Results indicate that the positioning error of an interference source based on two consecutive satellite measurements is only 3.7 km.
查看更多>>摘要：In ground-based L-band radar sea clutter, Bragg scattering caused by short gravity waves on the sea surface frequently exhibits azimuthal dependence, with higher order Bragg-scattering spectra clearly visible alongside the first-order spectrum. Recent measurements from an L-band airborne moving target detection (MTD) radar, operating in side-looking mode with HH polarization at medium incident angles (30°–60°), near the Zhoushan Fishing Ground in Ningbo, China, also reveal azimuth-dependent and multipeak characteristics, that pose challenges for target detection within the endo-clutter region. To better understand the clutter characteristics in L-band airborne MTD radar, this article investigates the modeling of sea clutter Doppler spectra under medium incident angles ranging from 30° to 60°. Using the small slope approximation (SSA) incorporating a time-varying rough sea surface with spikes, a systemic expression for the Doppler spectrum, accounting for the sea clutter space-time coupling, is derived. Specifically, the Doppler spectrum related to the sea surface is expressed as an azimuth-dependent underlying spectrum weighted by the antenna beam (i.e., the spatial spectrum), while the spectrum due to spikes is represented as a convolution of the spatial spectrum with an azimuth-independent underlying spectrum. Each individual spectrum is characterized with Gaussian profiles, forming the basis of a comprehensive spectrum model that can successfully capture an azimuth-independent peak and approximately 2–7 or more azimuth-dependent peaks in the real-world sea-clutter Doppler spectra. Note that the proposed model is directed against sea echoes with azimuth-dependent scattering properties from the main lobe of the two-way antenna pattern, and thus, suitably characterizes the corresponding sea-clutter Doppler features.
Mohammad J. TourianOmid ElmiShahin KhaliliJohannes Engels...
1-19页
查看更多>>摘要：In the 30 years of its availability, satellite altimetry has established itself as an important tool for understanding the Earth system. Originally developed for oceanography and geodesy, it has also proven valuable for monitoring water level variation of lakes and rivers. However, when using altimetry for inland waters, there is always a critical issue: retracking, i.e., the procedure in which the range from the satellite to the water surface is (re)estimated. The current retracking methods heavily rely on single waveforms, which results in a high sensitivity to every individual peak in the waveform and in a strong dependency on the waveform’s shape. Here, we propose the bin-space-time (BiST) retracking method that moves beyond finding a single point in a 1-D waveform and instead seeks a retracking line within a 2-D radargram, for which the temporal information over different cycles is also considered. The retracking line divides the radargram into two segments: the left (Front) and the right-hand side (Back) of the retracking line. Such a segmentation approach can be interpreted as a binary image segmentation problem, for which spatiotemporal information can be incorporated. We follow a Bayesian approach, exploiting a probabilistic graphical model known as a Markov random field (MRF). There, the problem is arranged as a maximum a posteriori estimation of an MRF (MAP-MRF), which means finding a retracking line that maximizes a posterior probability density or minimizes a posterior energy function. Our posterior energy function is obtained by a prior energy function and a likelihood energy function, both of them depending on signal intensity and bin: 1) the prior: the bin-space energy function defined between first-order neighboring pixels of a radargram modeling the spatial dependency between their labels for given intensities and bins and 2) the likelihood: the temporal energy function of a pixel for labeling Front or Back given its overall temporal evolution. The realization of the field with the minimum sum of the bin-space and the temporal energy functions is then found through the maxflow algorithm. Consequently, the retracking line, which defines the boundary between the Back and Front region, is obtained. We apply our method to both pulse-limited and synthetic aperture radar (SAR) altimetry data over nine lakes and reservoirs in the USA with different sizes and different altimetry characteristics. The resulting water level time series are validated against in situ data. Across the selected case studies, on average, the BiST retracker improves the root-mean-square error (RMSE) by approximately 0.5 m compared to the best existing retracker. The main benefit of the proposed retracker, which operates in bin, space, and time domains, is its robustness against unexpected waveform variations, making it suitable for diverse inland water surfaces.
查看更多>>摘要：Recently, studies have shown the potential of integrating field-type iterative methods with deep learning (DL) techniques in solving inverse scattering problems (ISPs). In this article, we propose a novel variational Born iterative network (VBIM-Net), to solve the full-wave ISPs with significantly improved structural rationality and inversion quality. The proposed VBIM-Net emulates the alternating updates of the total electric field and the contrast in the variational Born iterative method (VBIM) by multiple layers of subnetworks. We embed the analytical calculation of the contrast variation into each subnetwork, converting the scattered field residual into an approximate contrast variation and then enhancing it by a U-Net, thus avoiding the requirement of matched measurement dimension and grid resolution as in existing approaches. The total field and contrast of each layer’s output are supervised in the loss function of VBIM-Net, imposing soft physical constraints on the variables in the subnetworks, which benefits the model’s performance. In addition, we design a training scheme with extra noise to enhance the model’s stability. Extensive numerical results on synthetic and experimental data both verify the inversion quality, generalization ability, and robustness of the proposed VBIM-Net. This work may provide some new inspiration for the design of efficient field-type DL schemes.
查看更多>>摘要：Moving infrared small target detection (ISTD) is critical for various applications, e.g., remote sensing and military. Due to tiny target size and limited labeled data, accurately detecting targets is highly challenging. Currently, existing methods primarily focus on fully supervised learning, which relies heavily on numerous annotated frames for training. However, annotating a large number of frames for each video is often expensive, time-consuming, and redundant, especially for low-quality infrared images. To break through traditional fully supervised framework, we propose a new semi-supervised multiview prototype (S2MVP) learning scheme that incorporates motion reconstruction (MR). In our scheme, we design a bitemporal motion perceptor (BMP) based on bidirectional convolutional gate recurrent unit (ConvGRU) cells to effectively model the motion paradigms of targets by perceiving both forward and backward. Additionally, to explore the potential of unlabeled data, it generates the multiview feature prototypes of targets as soft labels to guide feature learning by calculating cosine similarity. Imitating human visual system (HVS), it retains only the feature prototypes of recent frames. Moreover, it eliminates noisy pseudo-labels to enhance the quality of pseudo-labels through anomaly-driven pseudo-label filtering (APF). Furthermore, we develop a target-aware MR loss to provide additional supervision and prevent the loss of target details. To our best knowledge, the proposed S2MVP is the first work to utilize large-scale unlabeled video frames to detect moving infrared small targets. Although 10% labeled training samples are used, the experiments on three public benchmarks (DAUB, ITSDT-15K and IRDST) verify the superiority of our scheme compared to other methods. Source codes are available at https://github.com/UESTC-nnLab/S2MVP.
查看更多>>摘要：It is challenging to achieve bistatic inverse synthetic aperture radar (Bi-ISAR) imaging and scaling for maneuvering targets. In the Bi-ISAR system, high-order translational and spatial variant (SV) rotational motion errors induced by the target’s maneuvering characteristics and time-varying bistatic angle would severely blur the imaging result. Moreover, both range and cross-range scaling (2-D scaling) are needed to exploit the size information of the target in practical applications. By parametric global modeling and extracting the coupling relationship between the target’s rotational motion and time-varying bistatic angle, this article presents a new Bi-ISAR imaging framework to achieve the integration of high-order motion compensation and 2-D scaling (IHOMC-2S) for maneuvering targets. First, a multidimensional motion errors signal model is developed. Based on the established parametric global model, a joint high-order translational motion compensation and SV autofocus method (JHTSVA) is presented via parametric minimum entropy optimization with the quasi-Newton solver. Then, with the estimated optimal parameters, the effective rotational velocity (ERV) and distortion coefficient can be estimated simultaneously by solving a 1-D unconstrained optimization problem. In addition, in order to successfully perform the 2-D scaling, a data-driven initial bistatic angle estimation method based on the linked feature scatterers is given. It is worth noting that the linear geometric distortion must be corrected before 2-D scaling, otherwise the sheared Bi-ISAR image may lead to an unreliable target recognition result. Finally, underpinned by the efficient and robust approach, IHOMC-2S can achieve high-resolution Bi-ISAR imaging and scaling for maneuvering targets avoiding the selection of prominent scatterers. Several experiments confirm the feasibility and robustness of the proposed algorithm.
查看更多>>摘要：Infrared small target detection (IRSTD) has been widely applied and developed in military and civilian fields, playing a vital role. Despite the extensive research foundation of traditional manual feature-based methods, they are still constrained by the inherent problem of infrared small targets lacking prior features. In recent years, the advancement of deep learning methods has enriched the research landscape in this field, yet they are still constrained by the imbalance of positive and negative samples between the target and the background. To address these issues, we propose a novel prior guided dense nested network (PGDN-Net), which ingeniously integrates traditional manual features with a deep learning network model. First, three prior features are extracted, including the high-order Riesz transform feature, the compactness and heterogeneity feature (CH), and the corner feature of the structure tensor (ST). Then, these features are input into a dense nested network for guidance, supported by a two-orientation attention aggregation module and a channel and spatial attention module. Different features play their respective guiding roles in different depths of the network. Through multiple attention mechanisms and feature fusion operations on the interested target area, the extraction and preservation of target features can be improved, while easily removing irrelevant backgrounds. Experiments on public datasets demonstrate the effectiveness and progressiveness of our PGDN-Net. Compared with other state-of-the-art methods, it achieves better performance in background suppression, target enhancement, probability of detection, and false alarm rate. In addition, the PGDN-Net model can effectively maintain and restore the original shape of the target while performing robust detection, which is beneficial for subsequent fine-grained recognition tasks.
查看更多>>摘要：Transformer-based deep learning (DL) methods have gradually been advocated for remote sensing (RS) image semantic segmentation due to the great global modeling capability. Nevertheless, Transformer-based DL methods have not yet been sufficiently explored on the large-scale hyperspectral image (HSI) semantic segmentation. Current algorithms lack a comprehensive consideration of the impact of positional encoding (PE) interpolation when constructing Transformer-based decoders. Moreover, existing segmentation heads usually directly concatenate multiscale features to achieve segmentation, which ignores the inherent semantic differences between different features. To address the above issues, a U-shaped multimixed Transformer network (UM2Former) is proposed for large-scale HSI semantic segmentation. First, a weight encoder consisting of two modules, the overlap-down and the channel-weight, is built to extract hierarchical discriminative spectral-spatial features and decrease spectral redundancy. Second, the proposed multimixed Transformer block (MMTB) develops a PE-free module, spatial-feature-retention attention (SFRA) mechanism, in which “multimixed” represents the global dependency modeling of each pixel with the retented average spatial characteristics of different locations in the input feature maps. Finally, a linear fuse segmentation head (LFSH) is designed to align semantic information among multiscale feature maps and achieve accurate segmentation. Experiments were conducted in single cities and the entire large-scale WHU-OHS HSI dataset. The segmentation results indicated that the proposed method achieved higher accuracy compared to the existing semantic segmentation methods, with performance improvements of 17.80% and 4.16% in terms of intersection over union (mIoU) and overall accuracy (OA), respectively. The source code will be available at https://github.com/ZhaohuiXue/ UM2Former.
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