Oil spill GF-1 remote sensing image segmentation using an evolutionary feedforward neural network

Fan, Jianchao; Zhao, Dongzhi; Wang, Jun

doi:10.1109/ijcnn.2014.6889519

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…The interview period is 4 days. GF-1 satellite data has the characteristics of high resolution, wide width, and short return period, and it can be widely used in agricultural remote sensing, environmental monitoring, and other fields [25,26].…”

Section: Methodsmentioning

confidence: 99%

Object-Level Double Constrained Method for Land Cover Change Detection

Wang

Ren

2018

Sensors

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Land cover change detection based on remote sensing has become increasingly important for protecting the ecological environment. Spatial features of images can be extracted by object-level methods. However, the computational complexity is high when using many features to detect land cover change. Meanwhile, single-constrained change detection (SCCD) methods produce non-objective and inaccurate results. Therefore, we proposed a land cover change detection method: the object-level double constrained change detection (ODCD) method. First, spectral and spatial features were calculated based on multi-scale segmentation results. Second, using the significant difference test (SDT), feature differences among all categories were calculated, and the features with more significant differences were considered as the optimal features. Third, the maximum Kappa coefficient was used as the criterion for determining the optimal change intensity and correlation coefficient. Finally, the ODCD was validated using GF-1 satellite images on March 2016 and February 2017 in north Beiqijia Town, Beijing. Using optimal feature selection, the dimension of features was reduced from 26 to 12. Compared with SCCD methods, the result of the ODCD was more reliable and accurate. Its overall accuracy was 10% higher, overall error was 27% lower, and the Kappa coefficient was 0.22 higher. In conclusion, the ODCD is effective for land cover change detection and can improve computational efficiency.

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Section: Methodsmentioning

confidence: 99%

Object-Level Double Constrained Method for Land Cover Change Detection

Wang

Ren

2018

Sensors

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Detection and Monitoring of Oil Spills Using Moderate/High-Resolution Remote Sensing Images

Liu

Cui

Liu³

et al. 2017

Arch Environ Contam Toxicol

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Current marine oil spill detection and monitoring methods using high-resolution remote sensing imagery are quite limited. This study presented a new bottom-up and top-down visual saliency model. We used Landsat 8, GF-1, MAMS, HJ-1 oil spill imagery as dataset. A simplified, graph-based visual saliency model was used to extract bottom-up saliency. It could identify the regions with high visual saliency object in the ocean. A spectral similarity match model was used to obtain top-down saliency. It could distinguish oil regions and exclude the other salient interference by spectrums. The regions of interest containing oil spills were integrated using these complementary saliency detection steps. Then, the genetic neural network was used to complete the image classification. These steps increased the speed of analysis. For the test dataset, the average running time of the entire process to detect regions of interest was 204.56 s. During image segmentation, the oil spill was extracted using a genetic neural network. The classification results showed that the method had a low false-alarm rate (high accuracy of 91.42%) and was able to increase the speed of the detection process (fast runtime of 19.88 s). The test image dataset was composed of different types of features over large areas in complicated imaging conditions. The proposed model was proved to be robust in complex sea conditions.

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Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints

Huang

2020

Cogn Comput

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Oil spill GF-1 remote sensing image segmentation using an evolutionary feedforward neural network

Cited by 5 publications

References 18 publications

Object-Level Double Constrained Method for Land Cover Change Detection

Object-Level Double Constrained Method for Land Cover Change Detection

Detection and Monitoring of Oil Spills Using Moderate/High-Resolution Remote Sensing Images

Collective Neurodynamic Optimization for Image Segmentation by Binary Model with Constraints

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