Edge detection method of remote sensing images based on mathematical morphology of multi-structure elements

Lin, Hao; Du, Peijun; Zhao, Chenwei; Ning, Shu

doi:10.1007/s11769-003-0057-9

Cited by 23 publications

(11 citation statements)

References 5 publications

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“…Subsequently, texture, morphology and other features have been gradually introduced. As a result, image segmentation and classification algorithms such as threshold segmentation, edge extraction, decision tree and support vector machine are used to effectively extract water body information from medium and high-resolution images [6][7][8][9][10][11]. In view of the spectral characteristics of water bodies determined through advanced very high resolution radiometer (AVHRR), thematic mapper (TM) and other multi-spectral remote sensing data, a variety of water body indices have been proposed and developed.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Song

Liu

et al. 2020

Sensors

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High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.

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Section: Introductionmentioning

confidence: 99%

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Song

Liu

et al. 2020

Sensors

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“…This algorithm can weaken the correlation between multispectral bands, thus reducing information redundancy, highlighting more useful or discriminative information in the data itself, thus increasing the effectiveness of land cover (features) object recognition based on deep learning. The difference between GS and PCA is that the information contained in the PCA is mainly in the first component with the most information, and its information decreases in turn in the remaining color components, while the components transformed by Gram-Schmidt are only orthogonal, and the amount of information contained in each component is not significantly different [40]. Therefore, the GS transform can preserve the spectral information of original multispectral images and the spatial texture features of panchromatic images to the greatest extent, so as to solve the problem of excessive concentration of the first component in PCT.…”

Section: Image Fusion Methodsmentioning

confidence: 99%

The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning

Song

Liu

et al. 2019

Remote Sensing

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The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District of Beijing, we apply the fusion results to conduct the experiments of object recognition of typical urban features based on deep learning. Furthermore, we perform a quantitative analysis for the existing pixel-based mainstream fusion methods of IHS (Intensity-Hue Saturation), PCS (Principal Component Substitution), GS (Gram Schmidt), ELS (Ehlers), HPF (High-Pass Filtering), and HCS (Hyper spherical Color Space) from the perspectives of spectrum, geometric features, and recognition accuracy. The results show that there are apparent differences in visual effect and quantitative index among different fusion methods, and the PCS fusion method has the most satisfying comprehensive effectiveness in the object recognition of land cover (features) based on deep learning.

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“…46 The size of the structural element is usually determined according to experimental results and the characteristics of the original signal. 47 In our experiments, ball, disk, diamond, rectangle, and square structural elements were used for comparative analysis. In addition, based on the Daubechies wavelet and Symlet wavelet methods, denoising results of the WT with four-layer wavelet decomposition are discussed below.…”

Section: Proposed Methodsmentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Edge detection method of remote sensing images based on mathematical morphology of multi-structure elements

Cited by 23 publications

References 5 publications

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

The Comparison of Fusion Methods for HSRRSI Considering the Effectiveness of Land Cover (Features) Object Recognition Based on Deep Learning

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

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