Although a chaffer sieve is used to separate impurities from kernels during corn harvesting, it is often clogged by impurities, and this has a negative impact on the separating performance. Accurate imagerecognition ...
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Although a chaffer sieve is used to separate impurities from kernels during corn harvesting, it is often clogged by impurities, and this has a negative impact on the separating performance. Accurate imagerecognition is the primary step in automatic working parameter adjustment that helps avoid clogging. Unfortunately, meshes of sieve underneath the impurities cannot be recognized using existing algorithms, and the clogging area of mesh and the impurity in background cannot be distinguished easily. To address this issue, a low-rank-constraint-based sieve clogging recognition (LSCR) algorithm is proposed in this study. Unlike existing algorithms, the position and shape of meshes are accurately estimated using the low-rank optimization strategy, and there is no need of training samples or complete information related to the mesh outline from the target images. The clogging areas are then determined based on the difference in relative reflectance. The experimental results demonstrate that the overall recognition accuracy in pixel level using the LSCR algorithm reaches 0.943 for the test scenes, which is significantly higher than that of the existing algorithms. LSCR can be potentially used for online chaffer-sieve clogging detection in corn harvesters.
The rapid development of artificial intelligence technology has brought profound technical changes to many traditional industries, among which intelligent transportation has become a hot spot for development in the tr...
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The rapid development of artificial intelligence technology has brought profound technical changes to many traditional industries, among which intelligent transportation has become a hot spot for development in the traditional transportation field. remotesensing license plate imagerecognition technology is extensively emphasized in such domains as intelligent transportation and intelligent vehicle management. However, in the practical traffic environment, low visibility scenes caused by complex environmental factors such as rain, snow, haze and cloudy days influence the recognition and classification of license plates, while the distortion of license plate images that may be caused by irregular movements of vehicles bring challenges to license plate recognition classification. The CNN-CatBoost model proposed in this paper divides the license plate recognition classification into two stages. The first stage uses the excellent performance of convolutional neural network in processingimage data to extract various license plate image features;the second stage uses the CatBoost module to further process the image feature data and finally obtain the remotesensing license plate information. The model achieves outstanding results in the experiments and has practical application value. Through comparison with other network models, the CNN-CatBoost model proposed in this paper has superior performance.
Airplane detection and recognition in the high-resolution remotesensingimages (RSIs) remain a challenging task due to the factors of multiple view angles, multiple scales, multiple orientations, etc. This article pr...
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Airplane detection and recognition in the high-resolution remotesensingimages (RSIs) remain a challenging task due to the factors of multiple view angles, multiple scales, multiple orientations, etc. This article proposes an adaptive component discrimination network for airplane detection and recognition in RSIs, which focuses on various scales from global to local, making full use of the overall contour as well as the dominant component features of airplanes. First, a standardization processing module is proposed for image projection conversion and resolution uniform to alleviate the confusion of different types of airplanes in different resolution images. Second, the rotatable boundingbox-based pyramid network is utilized to extract candidate airplane coordinates and categories. Furthermore, an adaptive aircraft component discrimination method is established for confusing few-shot airplane targets recognition, which consists of a target orientation adaptive adjustment module (OAAM) and a component discrimination module (CDM). OAAM obtains airplanes with the same orientations by predicting the orientation of the slices and rotating them adaptively. All the uniformed slices are then fed into the CDM for dominant components detection, which corrects the target preclassification results, improving the classification performance. Experiments conducted on the 2020 Gaofen Challenge demonstrate the efficacy and superiority of the proposed method.
Polarimetric Synthetic Aperture Radar (PolSAR) image classification is an important research content in the field of remotesensing. However, the lack of effective labeled data and the serious class distribution shift...
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Changes in the environmental conditions of Land Use/Land Cover can also significantly influence surface water, mostly due to the dramatically increased biophysical characteristics of the land surfaces. Therefore, extr...
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Changes in the environmental conditions of Land Use/Land Cover can also significantly influence surface water, mostly due to the dramatically increased biophysical characteristics of the land surfaces. Therefore, extracting multi-scale surface water bodies in urbanized areas is essential. Besides, the past relationships of surface water bodies mostly used low-resolution images. However, the water surface mapping and spatial analysis of multi-scale structure changes in urban areas with high-resolution satellite images have not yet been studied. The present study focused on multi-scale extraction and spatial analysis of growth pattern changes in urban water bodies using Sentinel-2 MSI imagery. Satellite data for the study were obtained from the location map of Chhattisgarh, in the central part of India. The available Sentinel-2 images covering the study area over the period 2012-2020. Initially, we applied image pre-processing steps to remove the shadow noise and distortions and convert the images into a suitable form for mapping. Then, the water surface mapping process combines multi-band water indices (Normalized Difference Vegetation Index and Normalized Difference Water Index (NDWI), Modified NDWI, Urban Difference WI, and Urban Difference Shadow Index and object-oriented methods to extract multi-scale water body information using high-resolution images. Study periods for change analysis were divided into sets. (a) Inter-annual variation is evaluated using the Water Area Frequency Index (WAFI) covering the entire study area over the time period 2012-2020. (b) The spatial variation of land use patterns (i.e., changes in the growth pattern of different scales) in the selected water areas is assessed using eight landscape metrics over three different time periods (2012, 2015, and 2020). These two variations in the WAFI layer are post-processed to determine three multi-scale water scenarios (rivers, streams, canals, and reservoirs): (1) Artificial Waterway (AW of cana
In recent years, high spatial resolution remotesensing technology has made significant progress. High resolution remotesensing satellites provide great convenience for high-quality image acquisition. In order to ada...
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In recent years, high spatial resolution remotesensing technology has made significant progress. High resolution remotesensing satellites provide great convenience for high-quality image acquisition. In order to adapt to changes in the appearance of the target, mainstream tracking algorithms often use patternrecognition methods to build a target appearance model with learning capabilities, and use the image frames acquired during the tracking process to update the appearance model. This paper mainly studies the object-oriented remotesensingimage information extraction method based on multi-classifier combination and deep learning algorithm. In this paper, we use the splitting mechanism of the tree structure to retain the appearance model with diversity, and through the integrated learning integration strategy, the target position is collaboratively predicted. Through the comparative analysis on the OTB and VOT platforms, the algorithm works well when the requirements of the tracking standards are low (the accuracy threshold is greater than 20 pixels and the success threshold is less than 0.4 pixels). The experimental results in this paper show that compared with other advanced classification methods, the proposed method shows better generalization performance in accuracy, recall, f-measure, g-mean and AUC. (C) 2020 Elsevier B.V. All rights reserved.
Hyperspectral image (HSI) super-resolution addresses the problem of fusing a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution HSI (HR-HSI). Tensor analysis ha...
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Hyperspectral image (HSI) super-resolution addresses the problem of fusing a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI) to produce a high-resolution HSI (HR-HSI). Tensor analysis has been proven to be an efficient method for HSI processing. However, the existing tensor-based methods of HSI super-resolution (HSI-SR) like the tensor train and tensor ring decomposition only establish an operation between adjacent two factors and are highly sensitive to the permutation of tensor modes, leading to an inadequate and inflexible representation. In this letter, we propose a novel method for HSI-SR by utilizing the specific properties of high-order tensors in fully-connected tensor network decomposition (FCTN). The proposed method first tensorizes the target HR-HSI into a high-order tensor that has multiscale spatial structures. Then, a coupled FCTN model is proposed to fuse the corresponding high-order tensors of LR-HSI and HR-MSI. Moreover, a weighted-graph regularization (WGR) is imposed on the spectral core tensors to preserve spectral information. In the proposed model, the superiorities of the FCTN lie in the outstanding capability for characterizing adequately the intrinsic correlations between any two modes of tensors and the essential invariance for transposition. Experimental results on three datasets show the effectiveness of the proposed approach as compared to other HSI-SR methods.
The huge computing and storage requirements of deep convolutional neural networks (DCNNs) limit their application on edge computing devices. In this article, we propose an attention mechanism based on the feature map ...
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ISBN:
(数字)9781665427920
ISBN:
(纸本)9781665427920
The huge computing and storage requirements of deep convolutional neural networks (DCNNs) limit their application on edge computing devices. In this article, we propose an attention mechanism based on the feature map quality evaluation algorithm (IQE). The knowledge distillation method based on the IQE attention mechanism uses the IQE method to identify important knowledge in the pre-trained SAR target recognition deep neural network. Then in the process of knowledge distillation, the lightweight network is forced to focus on the learning of important knowledge. Through this mechanism, the method proposed in this paper can efficiently transfer the knowledge of the pre-trained SAR target recognition network to the lightweight network, which makes it is possible to deploy the SAR target recognition algorithm on the edge computing platform. Comparison experiments with several commonly used knowledge distillation methods have proved the effectiveness of our proposed method. In addition, we also verified the performance of the lightweight network obtained by our method on the edge platform based on the K210 processor.
Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. I...
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ISBN:
(纸本)9783031546044;9783031546051
Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. In order to develop an efficient and robust airborne dust monitoring algorithm, various challenges have to be overcome. Airborne dust may be opaque as well translucent, can vary heavily in density, and its boundaries are fuzzy. Also, dust may be hard to distinguish from other atmospheric phenomena such as fog or clouds. To cover the demand for a performant and reliable approach for monitoring airborne dust, we propose DustNet, a dust density estimation neural network. DustNet exploits attention and convolutional-based feature pyramid structures to combine features from multiple resolution and semantic levels. Furthermore, DustNet utilizes highly aggregated global information features as an adaptive kernel to enrich high-resolution features. In addition to the fusion of local and global features, we also present multiple approaches for the fusion of temporal features from consecutive images. In order to validate our approach, we compare results achieved by our DustNet with those results achieved by methods originating from the crowd-counting and the monocular depth estimation domains on an airborne dust density dataset. Our DustNet outperforms the other approaches and achieves a 2.5% higher accuracy in localizing dust and a 14.4% lower mean absolute error than the second-best approach.
This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and ...
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ISBN:
(纸本)9798350301298
This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and monoview images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geolocations in New York City. To alleviate the substantial pixel-wise annotation efforts, we propose an efficient street-view image annotation pipeline that leverages the existing label maps of satellite view and the transformation relations between different views (satellite, panorama, and mono-view). With the new OmniCity dataset, we provide benchmarks for a variety of tasks including building footprint extraction, height estimation, and building plane/instance/fine-grained segmentation. Compared with existing multi-level and multi-view benchmarks, OmniCity contains a larger number of images with richer annotation types and more views, provides more benchmark results of state-of-the-art models, and introduces a new task for fine-grained building instance segmentation on street-level panorama images. Moreover, OmniCity provides new problem settings for existing tasks, such as cross-view image matching, synthesis, segmentation, detection, etc., and facilitates the developing of new methods for large-scale city understanding, reconstruction, and simulation. The OmniCity dataset as well as the benchmarks will be released at https://***/omnicity/.
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