This article studies the effect of transceiver array aperture size on the inversion ability of the linear integral equation-based solver for the electromagnetic (EM) reconstruction of 2-D scatterers embedded inside a planarly multilayered medium. The investigation is performed in three steps. First, we derive the analytical relationship between the spectra of the scattered electric fields at the receiver array and the reconstructable 2-D scatterer spectrum, which is composed of four different spectral components. This is completely different from the single spectral component for the 2-D scatterer directly placed in a homogeneous subsurface region. Second, the singular value decomposition (SVD) is adopted to compute the discretized integral operator’s right-singular function whose spectrum can also reflect the reconstructable 2-D scatterer spectrum but with the wave attenuation and evanescent mode contribution taken into account. The obtained spectrum shows a “bandstop” feature in the vertical direction with a decrease in the transceiver array aperture size, which is totally different from the “bandpass” feature for the 2-D scatterer embedded in the homogeneous subsurface region. Third, the features of the reconstructable spectrum of the 2-D scatterer embedded inside a planarly multilayered medium, especially the “bandstop” feature, are validated in a series of numerical experiments.

Residential extraction based on deep learning approach is a significant task in Synthetic Aperture Radar (SAR) image processing. However, extremely limited SAR data brings great challenges to the data-driven method: 1) pixel-wise annotations are hard to obtain due to the expensive cost; 2) There is not any universal large-scale SAR image dataset with heterogeneous SAR images. In this paper, a novel residential extraction method based on similarity-aware multi-source alignment strategy is proposed to solve such problems. Firstly, we propose a weakly-supervised Multi-source Similarity-aware Extraction Network (MSENet) to preserve the context dependency of pixels and improve the integrity of the extraction. Then, to tackle with the lack of training samples, a multi-source knowledge alignment strategy is proposed to learn transferrable knowledge from heterogeneous SAR datasets. Finally, affinity-guided optimization is introduced to refine the coarse maps with clear boundaries. Comprehensive experiments demonstrate the efficiency of our method.

Target extraction based on deep learning approaches is a significant task in Synthetic Aperture Radar (SAR) image processing. However, the lack of SAR image samples brings great challenges to the data-driven method. In this paper, a Saliency-guided Cross-domain Discrepancy Alignment strategy is proposed to solve this problem. Firstly, we propose a saliency-guided attention module, which utilizes the context-aware saliency knowledge to guide the feature extraction and improve the training efficiency. Secondly, we train the Saliency-guided Cross-domain Alignment Network (SCANet) by large-scale natural optical image dataset and tiny-scale SAR image dataset. Thirdly, based on the guidance of saliency attention, cross-domain representation alignment strategy is proposed to learn a latent representation which aligns feature distribution between the source and target domain. Finally, SCANet is more adaptive for SAR images and extracts targets more accurately. Comparison with state-of-the-arts and ablation experiments demonstrate the efficiency of our method, especially in complex conditions.

Target extraction is a significant task in Synthetic Aperture Radar (SAR) image processing. Recently, SAR target extraction with weak labels has attracted great attention due to the low labeling cost. However, weak-labeled SAR data brings great challenges to the data-driven methods: 1) location and structural information of the targets are lost in weak labels; 2) discrepancy of heterogeneous SAR images restricts the training efficiency of the model. In this paper, a novel Cross-domain Self-correlation Aware Network (CSANet) for SAR target extraction based on image-level weak labels is proposed to address such challenges. Firstly, the Cross-domain Representation Alignment (CRA) strategy is proposed to learn transferrable knowledge from heterogeneous SAR datasets. Through cross-domain alignment, invariant feature space is constructed to bridge the heterogeneous SAR data and improve the generalization performance of the model. Then, we propose a self-correlation aware extraction module with image-level weak labels, which only indicate whether the images contain the targets or not. Self-Correlation Module (SCM) is designed to preserve the context dependency of SAR pixels and compensate for the gap between weak labels and dense prediction. Finally, Affinity-Guided Optimization (AGO) is introduced to learn the inner-pixel affinity and refine the coarse extraction maps with clear boundaries. Comparison with state-of-the-arts and the ablation experiments demonstrate the efficiency of our method.

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often have texture detail loss and color distortion problems, especially in highlighted regions. This is mainly due to the rich texture and low intensity of UAV remote sensing images being ignored, which results in incorrect transmission estimation. In this paper, we propose a UAV remote sensing image dehazing method based on double-scale transmission optimization strategy. First, we propose a double-scale optimization strategy to estimate the transmission map with more accurate texture details and color preservation, especially in highlighted regions of hazy UAV images that are most severely distorted. Second, a UAV-adaptive haze-line prior algorithm is proposed to address the large scene depth and low intensity of UAV remote sensing images. Finally, we introduce a luminance-weighted frequency domain saliency model to avoid texture detail loss and color distortions for better transmission optimization, especially in highlighted regions. Compared with state-of-the-art methods, our method shows better detail performance and visual effects, especially for UAV images with highlighted regions.

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.

Extracting region of interest (ROI) plays an important role in many computer vision tasks. Recently, deep methods have shown excellent performance, however, when it comes to remote sensing image (RSI) domain, which lacks pixel-level annotations, training often leads to under-fitting and low-accuracy. In this paper, we propose a novel ROI extraction model based on unsupervised cross-domain adaptation for RSIs. Firstly, we pretrain the network, RS- RoINet, by large-scale natural datasets to learn general features. Through top-down propagation mechanism, we combine global and local information to generate the accurate edge of extraction maps. Then, we introduce domain adaptation module to reduce the difference between natural domain and RSI domain. Data from both domains is transferred into Reproducing Kernel Hilbert Space to measure the domain distribution distance. Finally, the model is adaptive for RSIs and extracts ROI more accurately. Compared with recent fully-supervised state-of-the-arts, our unsupervised method shows outstanding performance.

Saliency detection is a newly emerging tool to extract target in image processing. However, due to the loss of color in synthetic aperture radar (SAR) images, the detection result using the traditional saliency analysis is not satisfying. Therefore, a new saliency-driven target detection model based on common visual feature clustering is introduced for multiple SAR images. Firstly, Markov Random Field is applied to extract intra-image saliency map. Secondly, intensity, texture and curve features are extracted from multiple SAR images as common visual features, which can effectively compensate for the lack of color information. And then fuzzy c-means is employed to construct inter-image saliency map. Finally, an effective fusion strategy is used to combine the intra-image saliency map with the inter-image saliency map to obtain the final common saliency map. The experimental results demonstrate that the proposed model outperforms most the state-of-the-art saliency detection models.