- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2018
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- deep learning
- remote sensing
- Moderate Resolution Imaging Spectroradiometer (MODIS)
- Optoelectronic Information Science and Engineering
- Ocean University of China
-
Advances in Microclimate Ecology Arising from Remote Sensing
摘要： Microclimates at the land–air interface affect the physiological functioning of organisms which, in turn, influences the structure, composition, and functioning of ecosystems. We review how remote sensing technologies that deliver detailed data about the structure and thermal composition of environments are improving the assessment of microclimate over space and time. Mapping landscape-level heterogeneity of microclimate advances our ability to study how organisms respond to climate variation, which has important implications for understanding climate-change impacts on biodiversity and ecosystems. Interpolating microclimate measurements and downscaling macroclimate provides an organism-centered perspective for studying climate–species interactions and species distribution dynamics. We envisage that mapping of microclimate will soon become commonplace, enabling more reliable predictions of species and ecosystem responses to global change.
关键词： microclimate,ecology,vegetation structure,climate change,remote sensing,biodiversity,thermal imaging,LiDAR
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Approach for Road Material Identification By Dual-Stage Convolutional Networks
摘要： The automatic extraction of road network information from satellite images is a meaningful and challenging task. Particularly, the analysis of road surface materials is very important during transport construction and maintenance. This paper proposes a method to extract road area and identify its corresponding materials. The approach is based on two different convolutional neural network structures. Firstly, we use encoder-decoder symmetric network structure to extract the candidate road area. Then the former outputs is processed by atrous convolutional network with very deep layers, in order to classify the covered substances through their representative spectral features. We also utilize the physical characteristics of road network to design morphology approach to enhance the completeness and formation of the road network. Experiential results on various satellite images show that the method can yields better accuracy and adaptability than other convolutional network based methods.
关键词： road region extraction,convolutional networks,image segmentation,Remote sensing,road material classification
更新于2025-09-23 15:22:29
-
A Sample Update-Based Convolutional Neural Network Framework for Object Detection in Large-Area Remote Sensing Images
摘要： This letter addresses the issue of accurate object detection in large-area remote sensing images. Although many convolutional neural network (CNN)-based object detection models can achieve high accuracy in small image patches, the models perform poorly in large-area images due to the large quantity of false and missing detections that arise from complex backgrounds and diverse groundcover types. To address this challenge, this letter proposes a sample update-based CNN (SUCNN) framework for object detection in large-area remote sensing images. The proposed framework contains two stages. In the first stage, a base model—single-shot multibox detector—is trained with the training data set. In the second stage, artificial composite samples are generated to update the training set. The parameters of the first-stage model are fine-tuned with the updated data set to obtain the second-stage model. The first- and second-stage models are evaluated using the large-area remote sensing image test set. Comparison experiments show the effectiveness and superiority of the proposed SUCNN framework for object detection in large-area remote sensing images.
关键词： large-area remote sensing images,sample update,object detection,Convolutional neural networks (CNNs)
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Review of Research on Registration of SAR and Optical Remote Sensing Image Based on Feature
摘要： Synthetic Aperture Radar(SAR) and optical remote sensing image registration is the prerequisite for image fusion and it is of important theoretical significance and practical value. The image registration methods are mainly divided into the methods based on feature, the methods based on Gray-scale and others. This article systematically sorts out feature-based optical and SAR remote sensing image registration techniques, summarizes all types of image registration, points out their advantages and disadvantages and predicts the prospects of their future.
关键词： synthetic aperture radar(SAR),image registration,remote sensing,feature-based
更新于2025-09-23 15:22:29
-
A Novel Neural Network for Remote Sensing Image Matching
摘要： Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.
关键词： neural network,image matching,remote sensing (RS) image,Deep learning (DL)
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A New Registration Algorithm for Multimodal Remote Sensing Images
摘要： Automatic registration of remote sensing images is a challenging problem in the applications of remote sensing. The multimodal remote sensing images have significant nonlinear radiometric differences, which lead to the failure of area-based and feature-based registration methods. In this paper, to overcome significant nonlinear radiometric differences and large scale differences of multimodal remote sensing images, we propose a new registration algorithm, which can meet the need of initial registration of multimodal remote sensing images that conform to similarity transformation model. Our synthetic and real-data experimental results demonstrate the effectiveness and good performance of the proposed method in terms of visualization and registration accuracy.
关键词： multi-scale atlas,phase correlation,Log-Gabor filter,Multimodal remote sensing images,image registration
更新于2025-09-23 15:22:29
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Circular Relevance Feedback for Remote Sensing Image Retrieval
摘要： Relevance feedback (RF) is a popular reranking technique, which aims at improving the performance of image retrieval by taking the user's opinions into account. In this paper, we introduce a new RF method, named circular relevance feedback (CRF), to enhance the behavior of remote sensing image retrieval (RSIR). Instead of the manual selection used in the common RF method, we adopt the active learning (AL) algorithm to select the samples from the initial results automatically in each RF iteration. Moreover, to ensure the selected images are representative and informative enough, we choose different AL algorithms to complete the different RF processes. Finally, the contributions of all AL-driven RF methods are integrated using a circular fusion scheme. The encouraging experimental results on the ground truth RS image archive illustrate that our CRF is useful for enhancing the performance of RSIR. In addition, compared with many existing RF methods, our CRF achieves improved behavior.
关键词： Relevance feedback,remote sensing image retrieval
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - An Adaptive Bandpass Filter Based on Temporal Spectrogram Analysis for Photoplethysmography Imaging
摘要： Photoplethysmography Imaging (PPGI) in the sense of remote vital sign measurement via camera has attracted high interest in recent years. The non-contact measurement principle allows the use in many health monitoring applications, like monitoring of newborns. Beyond that, there are interesting areas of application in the multimedia sector, such as measuring the reaction to multimedia content or heart rate based liveness detection for multimedia security. The derived signal of a PPGI algorithm is often referred as blood volume pulse signal (BVP). The signal corresponds to the optical signal of blood volume changes in the upper skin layers. Most current approaches use peak detection in frequency spectrum to estimate heart rate from BVP signals. However, we focus on heart rate computation based on beat-to-beat peak detection in time domain. In this paper, we present a method for adaptive bandpass filtering for PPGI based on temporal spectrogram analysis of the BVP signal with a sliding time window. The main goal of this new method is to further improve accuracy of beat-to-beat peak detection in time domain. The approach exploits the analysis of main frequency components of the BVP signal over time, to build a bandpass filter with adaptive cutoff frequencies in order to filter noise and interference. So far, state-of-the-art approaches have usually used fixed cut-off frequencies in the physiologically possible range of heart rate. The novelty of the proposed method lies in its simple but effective solution to reduce the influence of noise and interference in the PPGI signal to improve peak detection for heart rate estimation. We show the improvements applying the adaptive bandpass filter technique to four basic algorithmic approaches of PPGI, namely ICA, Chrominance, POS and 2SR and comparing against current state-of-the-art peak detection approaches. For the evaluation we used a database with videos of 26 subjects in 4 different scenarios, each lasting two minutes.
关键词： Photoplethysmography Imaging,time frequency analysis,signal processing,user reaction,peak detection,remote heart rate estimation,adaptive bandpass filtering
更新于2025-09-23 15:22:29
-
Domain Adaptation With Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification
摘要： Hyperspectral remote sensing image classification has drawn a great attention in recent years due to the development of remote sensing technology. To build a high confident classifier, the large number of labeled data is very important, e.g., the success of deep learning technique. Indeed, the acquisition of labeled data is usually very expensive, especially for the remote sensing images, which usually needs to survey outside. To address this problem, in this letter, we propose a domain adaptation method by learning the manifold embedding and matching the discriminative distribution in source domain with neural networks for hyperspectral image classification. Specifically, we use the discriminative information of source image to train the classifier for the source and target images. To make the classifier can work well on both domains, we minimize the distribution shift between the two domains in an embedding space with prior class distribution in the source domain. Meanwhile, to avoid the distortion mapping of the target domain in the embedding space, we try to keep the manifold relation of the samples in the embedding space. Then, we learn the embedding on source domain and target domain by minimizing the three criteria simultaneously based on a neural network. The experimental results on two hyperspectral remote sensing images have shown that our proposed method can outperform several baseline methods.
关键词： neural network,hyperspectral image classification,maximum mean discrepancy (MMD),remote sensing,Domain adaptation,manifold embedding
更新于2025-09-23 15:22:29
-
Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net
摘要： Deep learning (DL) has been successfully applied to single image super-resolution (SISR), which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) counterpart. Different from most current DL-based methods, which perform reconstruction in the spatial domain, we use a scheme based in the frequency domain to reconstruct the HR image at various frequency bands. Further, we propose a method that incorporates the wavelet transform (WT) and the recursive Res-Net. The WT is applied to the LR image to divide it into various frequency components. Then, an elaborately designed network with recursive residual blocks is used to predict high-frequency components. Finally, the reconstructed image is obtained via the inverse WT. This paper has three main contributions: 1) an SISR scheme based on the frequency domain is proposed under a DL framework to fully exploit the potential to depict images at different frequency bands; 2) recursive block and residual learning in global and local manners are adopted to ease the training of the deep network, and the batch normalization layer is removed to increase the flexibility of the network, save memory, and promote speed; and 3) the low-frequency wavelet component is replaced by an LR image with more details to further improve performance. To validate the effectiveness of the proposed method, extensive experiments are performed using the NWPU-RESISC45 data set, and the results demonstrate that the proposed method outperforms state-of-the-art methods in terms of both objective evaluation and subjective perspective.
关键词： residual learning,wavelet transform (WT),remote sensing image,super resolution,Recursive network
更新于2025-09-23 15:22:29