1,493 research outputs found
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
Vehicle recognition is a fundamental problem in SAR image interpretation.
However, robustly recognizing vehicle targets is a challenging task in SAR due
to the large intraclass variations and small interclass variations.
Additionally, the lack of large datasets further complicates the task. Inspired
by the analysis of target signature variations and deep learning
explainability, this paper proposes a novel domain alignment framework named
the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve
robustness under various operating conditions. Concisely, HDANet integrates
feature disentanglement and alignment into a unified framework with three
modules: domain data generation, multitask-assisted mask disentanglement, and
domain alignment of target features. The first module generates diverse data
for alignment, and three simple but effective data augmentation methods are
designed to simulate target signature variations. The second module
disentangles the target features from background clutter using the
multitask-assisted mask to prevent clutter from interfering with subsequent
alignment. The third module employs a contrastive loss for domain alignment to
extract robust target features from generated diverse data and disentangled
features. Lastly, the proposed method demonstrates impressive robustness across
nine operating conditions in the MSTAR dataset, and extensive qualitative and
quantitative analyses validate the effectiveness of our framework
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
Recently, change detection methods for synthetic aperture radar (SAR) images
based on convolutional neural networks (CNN) have gained increasing research
attention. However, existing CNN-based methods neglect the interactions among
multilayer convolutions, and errors involved in the preclassification restrict
the network optimization. To this end, we proposed a layer attention-based
noise-tolerant network, termed LANTNet. In particular, we design a layer
attention module that adaptively weights the feature of different convolution
layers. In addition, we design a noise-tolerant loss function that effectively
suppresses the impact of noisy labels. Therefore, the model is insensitive to
noisy labels in the preclassification results. The experimental results on
three SAR datasets show that the proposed LANTNet performs better compared to
several state-of-the-art methods. The source codes are available at
https://github.com/summitgao/LANTNetComment: Accepted by IEEE Geoscience and Remote Sensing Letters (GRSL) 2022,
code is available at https://github.com/summitgao/LANTNe
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic
tool for gastrointestinal (GI) diseases. However, due to GI anatomical
constraints and hardware manufacturing limitations, WCE vision signals may
suffer from insufficient illumination, leading to a complicated screening and
examination procedure. Deep learning-based low-light image enhancement (LLIE)
in the medical field gradually attracts researchers. Given the exuberant
development of the denoising diffusion probabilistic model (DDPM) in computer
vision, we introduce a WCE LLIE framework based on the multi-scale
convolutional neural network (CNN) and reverse diffusion process. The
multi-scale design allows models to preserve high-resolution representation and
context information from low-resolution, while the curved wavelet attention
(CWA) block is proposed for high-frequency and local feature learning.
Furthermore, we combine the reverse diffusion procedure to further optimize the
shallow output and generate the most realistic image. The proposed method is
compared with ten state-of-the-art (SOTA) LLIE methods and significantly
outperforms quantitatively and qualitatively. The superior performance on GI
disease segmentation further demonstrates the clinical potential of our
proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability:
https://github.com/longbai1006/LLCap
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