An Automatic Morphological Attribute Building Extraction Approach for Satellite High Spatial Resolution Imagery

Ma, Weixuan; Wan, Youchuan; Li, Jiayi; Zhu, Sa; Wang, Mingwei

doi:10.3390/rs11030337

Cited by 19 publications

(10 citation statements)

References 40 publications

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“…In recent years, morphological attribute profiles (MAPs) have been proven to have a strong ability to detect buildings in complex urban backgrounds, which has been one of the most effective spatial structure modelling methods for HRRS images. The morphological feature set of local area constructed by MAPs can be used to realize the multi-attribute and multi-scale expression of different ground objects, thus significantly improving the separability of buildings and other ground objects [5][6][7]. However, the following limitations must be overcome to realize high-precision, unsupervised building detection based on MAPs: (1) The potential building pixels are directly determined by the differential attribute profiles (DAPs) extracted from the differential of neighboring attribute profiles (APs), and morphological attribute profile (MAP) theory does not give a scale parameter setting using clear rules, so the requirement according to the scale of the original image is used to construct (on an adaptive basis) a reasonable parameter set.…”

Section: Introductionmentioning

confidence: 99%

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

et al. 2021

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High-resolution remote sensing (HRRS) images, when used for building detection, play a key role in urban planning and other fields. Compared with the deep learning methods, the method based on morphological attribute profiles (MAPs) exhibits good performance in the absence of massive annotated samples. MAPs have been proven to have a strong ability for extracting detailed characterizations of buildings with multiple attributes and scales. So far, a great deal of attention has been paid to this application. Nevertheless, the constraints of rational selection of attribute scales and evidence conflicts between attributes should be overcome, so as to establish reliable unsupervised detection models. To this end, this research proposes a joint optimization and fusion building detection method for MAPs. In the pre-processing step, the set of candidate building objects are extracted by image segmentation and a set of discriminant rules. Second, the differential profiles of MAPs are screened by using a genetic algorithm and a cross-probability adaptive selection strategy is proposed; on this basis, an unsupervised decision fusion framework is established by constructing a novel statistics-space building index (SSBI). Finally, the automated detection of buildings is realized. We show that the proposed method is significantly better than the state-of-the-art methods on HRRS images with different groups of different regions and different sensors, and overall accuracy (OA) of our proposed method is more than 91.9%.

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Section: Introductionmentioning

confidence: 99%

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

et al. 2021

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“…The minimum entropy deconvolution [13,14] designs the optimal filter to eliminate the random noise in the bearing impact signal under the condition of maximizing the kurtosis value. The adaptive filter [15,16] needs to introduce an additional noise signal and extract the bearing fault impact signal by designing an optimal filter. According to the characteristics of bearing fault vibration and impact, the matching trace [17,18] defines atoms to disassemble the vibration signal and extract the impact characteristic components.…”

Section: Introductionmentioning

confidence: 99%

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Miao

Zhao

Wang

et al. 2020

Sensors

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During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments. extract the characteristic signal. Cyclostationary signal analysis [21,22] relies on the cyclostationary characteristics of the bearing fault impulse signal to design a filter to eliminate random noise. A Wiener filter [23,24] is used to eliminate the stationary random noise in the fault impulse signal. A wavelet transform [25,26] first decomposes the vibration signal into different frequency bands, then defines the threshold to eliminate the noise components and reconstructs the characteristic signal. A Kalman filter [27,28] first establishes the vibration state model and signal observation model of rolling bearing, then iteratively eliminates the noise in the signal. Stochastic resonance [29,30] uses noise to enhance the fault characteristic component of the bearing vibration signal. All of the above mentioned methods are universal, can effectively eliminate background noise and interference components, and extract fault signals in specific environment, but they are not suitable for complex interference situations, and especially when the interference components in vibration signals are similar to fault signals, it is difficult to distinguish them by the above method, let alone eliminating interference components and extract fault signals. Blind source separation (BSS) technology can realize the separation of multiple aliased signals [1,31]. Meanwhile, blind source separation is not affected by the time and frequency overlap of source signals, and the separated output signal will not lose the weak feature information in the source signal.So far, many effective and distinctive blind source separation algorithms have been constructed. Typical algorithms include fast fixed-point [32] algorithms, natural gradient [28] algorithms, second-order blind identification (SOBI) [33] algorithms, equivalation adaptive separation via independence (EASI) [34] algorithms, and joint approximate diagonalization of eigenmatrices (JADE) [35] algorithms. When separating the noiseless mixed signals, these ...

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“…The multi-source data-based methods can use the same types of data, e.g., panchromatic band and multispectral imagery [7], optical imagery and light detection and ranging (LiDAR) data [4].Recently, the rapid development of DNNs has been focused in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks [11]. The majority of the articles published in the Special Issue propose classification based on the DNN [1][2][3][4][5][6]8,[11][12][13].There are also a small number of methods based on segmentation [6] and morphological filtering [15].Using aerial LiDAR data, Awrangjeb et al [16] introduce a new 3D roof reconstruction technique that constructs an adjacency matrix to define the topological relationships among the roof planes. This method then uses the generated building models to detect 3D changes in buildings.…”

mentioning

confidence: 99%

“…There are also a small number of methods based on segmentation [6] and morphological filtering [15].…”

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confidence: 99%

“…Among the other published papers, Cui et al [6] present a novel method coupling linear structures with three-dimensional geometric surfaces to automatically reconstruct 3D models using point cloud data from mobile laser scanning [6]. A new morphological attribute building index (MABI) and shadow index (MASI) are proposed in Ma et al [15] for automatically extracting building features from high-resolution remote sensing satellite images. In experiments, this method shows better performance than the two widely used supervised classifiers, namely the support vector machine (SVM) and random forest (RF).…”

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confidence: 99%

See 1 more Smart Citation

Editorial for Special Issue: “Remote Sensing based Building Extraction”

Awrangjeb

Wang

et al. 2020

Remote Sensing

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An Automatic Morphological Attribute Building Extraction Approach for Satellite High Spatial Resolution Imagery

Cited by 19 publications

References 40 publications

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

Automatic Building Detection from High-Resolution Remote Sensing Images Based on Joint Optimization and Decision Fusion of Morphological Attribute Profiles

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Editorial for Special Issue: “Remote Sensing based Building Extraction”

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