Clouds frequently affect optical remotesensing pictures throughout the gathering process, resulting in low-resolution images that affect judgment and subsequent use of ground data. Because of the thick cloud cover, t...
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Clouds frequently affect optical remotesensing pictures throughout the gathering process, resulting in low-resolution images that affect judgment and subsequent use of ground data. Because of the thick cloud cover, the ground surface information below is entirely incorrect. This kind of end-to-end image problem should not be dismissed as a simple task of image inpainting or image translation. Therefore, this paper proposes a multi-head self-attention module based on the encoding-decoding generative adversarial network, considering the redundant information of the deep network, furthermore this paper introduces Ghost convolution to effectively solve the influence of redundant feature maps in the network on the increase of time consumption and parameters. The method in this paper can solve the problem of cloud occlusion. By considering spatial information, it can better complete the prediction of cloud removal. It can reduce the amount of network calculations and parameters while maintaining the effect. In addition, Feature Fusion Module is proposed to integrate high-level features with low-level features, so that the network can extract enough feature information and better supplement the details to complete the cloud removal. The method in this paper has achieved excellent results on the RICE1 and RICE2 datasets.
Perceptual image hashing is pivotal in various imageprocessing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a no...
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Nowadays, object detection is an increasingly important technology in the field of remotesensingimageprocessing, which is applied to locate and identify high value ground objects in high resolution remotesensing i...
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Cloud detection is one of the critical tasks in remotesensingimage pre-processing and it has attracted extensive research interest. In recent years, deep neural networks based cloud detection methods have surpassed ...
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Cloud detection is one of the critical tasks in remotesensingimage pre-processing and it has attracted extensive research interest. In recent years, deep neural networks based cloud detection methods have surpassed the traditional methods (threshold-based methods and conventional machine learning-based methods). However, current approaches mainly focus on improving detection accuracy. The computation complexity and large model size are ignored. To tackle this problem, we propose a lightweight deep learning cloud detection model: Efficient Cloud Detection Network (ECDNet). This model is based on the encoder-decoder structure. In the encoder, a two-path architecture is proposed to extract the spatial and semantic information concurrently. One pathway is the detail branch. It is designed to capture low-level detail spatial features with only a few parameters. The other pathway is the semantic branch, which is mainly for capturing context features. In the semantic branch, a proposed dense pyramid module (DPM) is designed for multi-scale contextual information extraction. The number of parameters and calculations in DPM is greatly reduced by features reusing. Besides, a FusionBlock is developed to merge these two kinds of information. Then the extreme lightweight decoder recovers the cloud mask to the same scale as the input image step by step. To improve performance, boost loss is introduced without inference cost increment. We evaluate the proposed method on two public datasets: LandSat8 and MODIS. Extensive experiments demonstrate that the proposed ECDNet achieves comparable accuracy as the state-of-art cloud detection methods, and meantime has a much smaller model size and less computation burden. (C) 2022 Elsevier Ltd. All rights reserved.
Information regarding fishing grounds was needed to assist fishermen in their fishing activities. Information about the sea surface chlorophyll-a concentration (SSCC) and sea surface temperature (SST) could be used as...
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Information regarding fishing grounds was needed to assist fishermen in their fishing activities. Information about the sea surface chlorophyll-a concentration (SSCC) and sea surface temperature (SST) could be used as a reference to identify potential fishing zones (PFZ). The purpose of this study was to identify SST and SSCC data using MODIS (Moderate Resolution Imaging Spectroradiometer) satel-lite imagery for determining the PFZ and analyzing their distribution pattern seasonally. The determina-tion of the PFZ point was carried out by overlaying the SSCC and SST data based on the results of image data processing. The results showed that the distribution pattern of PFZ points in the Bangka Strait waters was predominantly found in the Banyuasin waters. The distribution pattern of PFZ points in the dry sea -son (June-August) and transition season II (September-November) had the same pattern and tended to dominate the coastal areas of the waters. The distribution patterns in the wet season (December-February) and transition season I (March-May) spread throughout the Bangka Strait waters. The most PFZ points were found in transition season I (636 PFZ points), while the minor PFZ points were found in transition season II (219 PFZ points). Integrating the remotesensing and GIS technique with statistical validation tests were useful and became a simple method for identifying the PFZ distribution. However, validating the PFZ distributions using the catch data was required.(c) 2021 National Authority of remotesensing & Space Science. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
The paper proposes a new approach for crowd movement type estimation in video by combining convolutional neural network and integral optical flow. At first, main notions of crowd detection and tracking are given. Seco...
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The paper proposes a new approach for crowd movement type estimation in video by combining convolutional neural network and integral optical flow. At first, main notions of crowd detection and tracking are given. Secondly, crowd movement features and parameters are defined. Three rules are proposed to identify direct crowd motion. Signs are presented for identifying chaotic crowd movement. Region movement indicators are introduced to analyze the movement of a group of people or a crowd. Thirdly, an algorithm of crowd movement types estimation using convolutional neural network and integral optical flow is proposed. We calculate crowd movement trajectories and show how they can be used to analyze behavior and divide crowds into groups of people. Experimental results show that with the help of convolutional neural network and integral optical flow crowd movement parameters can be calculated more accurately and quickly. The algorithm demonstrates stronger robustness to noise and the ability to get more accurate boundaries of moving objects.
Synthetic aperture radar (SAR) image similarity metric is at the core of SAR image interpretation techniques, however, it is still a challenging task due to complex nonlinear intensity, scale, and rotation differences...
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Synthetic aperture radar (SAR) image similarity metric is at the core of SAR image interpretation techniques, however, it is still a challenging task due to complex nonlinear intensity, scale, and rotation differences between SAR images and other remotesensingimages. This letter addresses this problem by proposing a novel similarity metric method for SAR images using structure and shape properties. The magnitude and orientation representation of the phase congruency model is first built based on the local phase of images. Then a new scale and rotation-invariant local binary pattern (SRI-LBP) descriptor is proposed using local structure and shape information. Finally, a similarity metric is defined using the symmetry Kullback Leibler divergence (SKLD) of the SRI-LBP descriptors. Numerical experiment results verify its robustness in terms of nonlinear intensity, scale, and rotation differences.
This study proposes the Jamming imagerecognition (JIR) Problem and a perspective solution with hybrid features. JIR aims to recognize the prototypical image in each class and differentiate two prototypical images for...
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The article introduces a scientific gateway to assess land surface temperatures using Landsat 8 and visible infrared imaging radiometer suite data. The gateway offers a selection of four temperature retrieval algorith...
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The article introduces a scientific gateway to assess land surface temperatures using Landsat 8 and visible infrared imaging radiometer suite data. The gateway offers a selection of four temperature retrieval algorithms and two interpolation methods to create time series. The evaluation of the gateway's performance in Armenia from May to October 2022 is illustrated. The research identifies the Price, Jimenez-Munoz, McMillin, and I05 Chanel algorithms as the most accurate nighttime temperature estimation. Additionally, these products exhibit a reasonable level of accuracy, with an average root mean squared error ranging from 2.42 to 2.45 degrees C and a coefficient of determination spanning from 0.82 to 0.95. The outcomes of this study bear significant relevance for diverse applications such as urban heat island analysis, environmental monitoring, and agricultural assessments.
Nonuniform haze on remotesensingimages degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remotesensingimages. To e...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Nonuniform haze on remotesensingimages degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remotesensingimages. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.
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