3D gray level co-occurrence matrix and its application to identifying collapsed buildings

Moya, Luis; Zakeri, Homa; Yamazaki, Fumio; Liu, Wen; Mas, Erick; Koshimura, Shunichi

doi:10.1016/j.isprsjprs.2019.01.008

Cited by 73 publications

(37 citation statements)

References 38 publications

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“…An important constraint is the availability of a pre-event image. To the best of our experience, a temporal baseline from few months to about a year performs fairly well to identify damages in urban areas [12][13][14][15][16][17][18]. However, other types of land use may exhibit constant and systematic changes through time.…”

Section: Introductionmentioning

confidence: 94%

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

et al. 2019

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Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SAR images from before and after the floods. (ii) The unawareness of the dynamics of the backscattering intensity through time in agricultural areas. (iii) The effect of the wavelength on agricultural targets. Furthermore, it is highly probable that such conditions might occur in future events. Our conclusions are supported by a field survey of 35 paddy fields located within the misclassified area and the analysis of Sentinel-1 time series data. In addition, in this paper, we propose a new parameter, which we named “conditional coherence”, that can be of help to overcome the referred issue. The new parameter is based on the physical mechanism of the backscattering on flooded and non-flooded agricultural targets. The performance of the conditional coherence as an input of discriminant functions to identify flooded and non-flooded agricultural targets is reported as well.

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Section: Introductionmentioning

confidence: 94%

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

et al. 2019

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“…In addition to polarimetric features, abundant texture features can also be extracted from PolSAR images. Because the texture features of buildings are obvious in PolSAR images, they can be used for building recognition [42]. Furthermore, there is a clear difference between the CB and the OB in texture.…”

Section: Texture Feature Extractionmentioning

confidence: 99%

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

2019

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After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

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“…When classifying high spatial resolution remote sensing imagery, information for both the target pixel and adjacent pixels must be considered [17,18]. Texture features are commonly used to express information related to adjacent pixels [19]; these can be extracted by methods including the gray level of co-occurrence matrix (GLCM) [20], Gabor filters [21], Markov random fields [22], and wavelet transforms [23]. As texture features can accurately express the spatial correlation between pixels, combining these with spectral features can effectively improve the classification accuracy of high-resolution remote sensing imagery [24].…”

Section: Introductionmentioning

confidence: 99%

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

Zhang

et al. 2020

Remote Sensing

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Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method using prior knowledge obtained from statistical analysis to refine CNN classification results, named post-processing CNN (PP-CNN). First, we used an improved RefineNet model to roughly segment remote sensing imagery in order to obtain the initial winter wheat area and the category probability vector for each pixel. Second, we used manual labels as references and performed statistical analysis on the class probability vectors to determine the filtering conditions and select the pixels that required optimization. Third, based on the prior knowledge that winter wheat pixels were internally similar in color, texture, and other aspects, but different from other neighboring land-use types, the filtered pixels were post-processed to improve the classification accuracy. We used 63 Gaofen-2 images obtained from 2017 to 2019 of a representative Chinese winter wheat region (Feicheng, Shandong Province) to create the dataset and employed RefineNet and SegNet as standard CNN and conditional random field (CRF) as post-process methods, respectively, to conduct comparison experiments. PP-CNN’s accuracy (94.4%), precision (93.9%), and recall (94.4%) were clearly superior, demonstrating its advantages for the improved refinement of edge areas during image classification.

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3D gray level co-occurrence matrix and its application to identifying collapsed buildings

Cited by 73 publications

References 38 publications

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

Improved Winter Wheat Spatial Distribution Extraction from High-Resolution Remote Sensing Imagery Using Semantic Features and Statistical Analysis

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