Synthetic aperture radar (SAR) is widely recognized as an effective tool for investigating oceanic processes, particularly internal wave dynamics, due to its ability to capture high-resolution imaging across diverse environmental conditions. However, speckle noise in SAR imagery poses significant challenges for accurately extracting critical parameters, such as wavefront characteristics and bright-dark distances, thereby hindering its application in detailed internal wave studies. To address this, a novel speckle filtering method is proposed, specifically designed to enhance the quality of SAR images capturing the feature of internal wave. First, a structural description matrix with complex values is introduced to encode pixel value correlations and local spatial features, enabling improved representation of wave-related contextual information within the SAR data. Second, a similarity testing framework based on the Wishart distribution is developed to identify and aggregate structurally similar regions, ensuring robust noise suppression while preserving essential structural parameters. The proposed method strikes a balance between speckle noise reduction and structural preservation, making it adaptable to a variety of SAR datasets. Experimental validation on SAR imagery of oceanic internal waves demonstrates the method’s effectiveness in retaining key internal wave information, while additional tests on terrestrial targets highlight its potential for generalization across different scenarios.

Oriented detectors have become the mainstream of object detection in remote-sensing images since they provide more precise bounding boxes and contain less background. However, there remain several challenges that restrict the detection performance and need to be tackled. This article focuses on the following two aspects: 1) numerous tiny objects in remote-sensing images pose a challenge for the detectors pursuing high recall and accurate localization and 2) specific categories with large aspect ratios and arbitrary angles also trouble the regression of the detectors. We attempt to alleviate the above problems by constructing a weak feature extraction network (WFEN) and a dual appearance-aware (DA) loss. Specifically, WFEN is used to extract hierarchical weight vectors for multiscale feature layers by employing a lightweight convolutional module, aiming to fuse activation features distributed in different layers and provide pure features for subsequent regression and classification. DA loss is tailored to regressions of tiny and slender objects by dynamically modulating the associated loss on objects with various appearances, which consists of two auxiliary losses, termed scale-aware loss${\mathcal {L}}_{S}$and aspect-ratio-aware loss${\mathcal {L}}_{A}$. These two components can contribute to each other, that is, the former provides more accurate features for detection tasks, while the latter can reciprocate the former by imposing constraints on crucial objects, and together constitute an appearance sensitivity detector (ASDet). Extensive experiments on three public datasets demonstrate that our ASDet outperforms all refine-stage detectors in terms of accuracy while maintaining the superior inference speed of single-stage counterparts.

Extracting 3D information from a single optical image is very attractive. Recently emerging self-supervised methods can learn depth representations without using ground truth depth maps as training data by transforming the depth prediction task into an image synthesis task. However, existing methods rely on a differentiable bilinear sampler for image synthesis, which results in each pixel in a synthetic image being derived from only four pixels in the source image and causes each pixel in the depth map to perceive only a few pixels in the source image. In addition, when calculating the photometric error between a synthetic image and its corresponding target image, existing methods only consider the photometric error within a small neighborhood of each single pixel and therefore ignore correlations between larger areas, which causes the model to tend to fall into the local optima for small patches. In order to extend the perceptual area of the depth map over the source image, we propose a novel multi-scale method that downsamples the predicted depth map and performs image synthesis at different resolutions, which enables each pixel in the depth map to perceive more pixels in the source image and improves the performance of the model. As for the locality of photometric error, we propose a structural similarity (SSIM) pyramid loss to allow the model to sense the difference between images in multiple areas of different sizes. Experimental results show that our method achieves superior performance on both outdoor and indoor benchmarks.