7 research outputs found
Oil spill detection using optical sensors: a multi-temporal approach
Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensorsβ capabilities in detecting oil spills at sea. The effectiveness of different sensorsβ resolution towards the detection of spills of different size, and the relevance of the sensorsβ revisiting time to track and monitor the evolution of the event is also investigated
ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΡΡ ΠΏΠΎ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
The article solves the problem of verifying oil spills on the water surfaces of rivers, seas and oceans using optical aerial photographs, which are obtained from cameras of unmanned aerial vehicles, based on deep learning methods. The specificity of this problem is the presence of areas visually similar to oil spills on water surfaces caused by blooms of specific algae, substances that do not cause environmental damage (for example, palm oil), or glare when shooting (so-called look-alikes). Many studies in this area are based on the analysis of synthetic aperture radars (SAR) images, which do not provide accurate classification and segmentation. Follow-up verification contributes to reducing environmental and property damage, and oil spill size monitoring is used to make further response decisions. A new approach to the verification of optical images as a binary classification problem based on the Siamese network is proposed, when a fragment of the original image is repeatedly compared with representative examples from the class of marine oil slicks. The Siamese network is based on the lightweight VGG16 network. When the threshold value of the output function is exceeded, a decision is made about the presence of an oil spill. To train the networks, we collected and labeled our own dataset from open Internet resources. A significant problem is an imbalance of classes in the dataset, which required the use of augmentation methods based not only on geometric and color manipulations, but also on the application of a Generative Adversarial Network (GAN). Experiments have shown that the classification accuracy of oil spills and look-alikes on the test set reaches values of 0.91 and 0.834, respectively. Further, an additional problem of accurate semantic segmentation of an oil spill is solved using convolutional neural networks (CNN) of the encoder-decoder type. Three deep network architectures U-Net, SegNet, and Poly-YOLOv3 have been explored for segmentation. The Poly-YOLOv3 network demonstrated the best results, reaching an accuracy of 0.97 and an average image processing time of 385 s with the Google Colab web service. A database was also designed to store both original and verified images with problem areas.Π ΡΡΠ°ΡΡΠ΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
ΡΠ΅ΠΊ, ΠΌΠΎΡΠ΅ΠΉ ΠΈ ΠΎΠΊΠ΅Π°Π½ΠΎΠ² ΠΏΠΎ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΡ Π΄Π°Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
Π½Π° ΡΠ°Π·Π»ΠΈΠ²Ρ Π½Π΅ΡΡΠΈ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
, Π²ΡΠ·Π²Π°Π½Π½ΡΡ
ΡΠ²Π΅ΡΠ΅Π½ΠΈΠ΅ΠΌ Π²ΠΎΠ΄ΠΎΡΠΎΡΠ»Π΅ΠΉ, Π²Π΅ΡΠ΅ΡΡΠ², Π½Π΅ ΠΏΡΠΈΠ½ΠΎΡΡΡΠΈΡ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΠ΅ΡΠ± (Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠ°Π»ΡΠΌΠΎΠ²ΠΎΠ΅ ΠΌΠ°ΡΠ»ΠΎ), Π±Π»ΠΈΠΊΠΎΠ² ΠΏΡΠΈ ΡΡΠ΅ΠΌΠΊΠ΅ ΠΈΠ»ΠΈ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ (ΡΠ°ΠΊ Π½Π°Π·ΡΠ²Π°Π΅ΠΌΡΠ΅ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΈΒ»). ΠΠ½ΠΎΠ³ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² Π΄Π°Π½Π½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΎΡ ΡΠ°Π΄Π°ΡΠΎΠ² Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π°ΠΏΠ΅ΡΡΡΡΠΎΠΉ (Synthetic Aperture Radar (SAR) images), ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ ΡΠΎΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ. ΠΠΎΡΠ»Π΅Π΄ΡΡΡΠ°Ρ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΡΠ±Π°, Π° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΡΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² ΠΊΠ°ΠΊ Π·Π°Π΄Π°ΡΠΈ Π±ΠΈΠ½Π°ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΊΠΎΠ³Π΄Π° ΡΡΠ°Π³ΠΌΠ΅Π½Ρ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π΅ΡΡΡ Ρ ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΠΌΠ΅ΡΠ°ΠΌΠΈ ΠΈΠ· ΠΊΠ»Π°ΡΡΠ° Π½Π΅ΡΡΡΠ½ΡΡ
ΠΏΡΡΠ΅Π½ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
. ΠΡΠ½ΠΎΠ²ΠΎΠΉ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠ»ΡΠΆΠΈΡ ΠΎΠ±Π»Π΅Π³ΡΠ΅Π½Π½Π°Ρ ΡΠ΅ΡΡ VGG16. ΠΡΠΈ ΠΏΡΠ΅Π²ΡΡΠ΅Π½ΠΈΠΈ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π²ΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎ Π½Π°Π»ΠΈΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²Π° Π½Π΅ΡΡΠΈ. ΠΠ»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΡΠΈ Π±ΡΠ» ΡΠΎΠ±ΡΠ°Π½ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ Π½Π°Π±ΠΎΡ Π΄Π°Π½Π½ΡΡ
ΠΈΠ· ΠΎΡΠΊΡΡΡΡΡ
ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅ΡΡΡΡΠΎΠ². Π‘ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΡΠ±Π°Π»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΡΡΡ Π²ΡΠ±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΊΠ»Π°ΡΡΠ°ΠΌ, ΡΡΠΎ ΠΏΠΎΡΡΠ΅Π±ΠΎΠ²Π°Π»ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°ΡΠ³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ²Π΅ΡΠΎΠ²ΡΡ
ΠΌΠ°Π½ΠΈΠΏΡΠ»ΡΡΠΈΡΡ
, Π½ΠΎ ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΎΡΡΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ (Generative Adversarial Network, GAN). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ ΠΈ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΎΠ²Β» Π½Π° ΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ Π΄ΠΎΡΡΠΈΠ³Π°Π΅Ρ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ 0,91 ΠΈ 0,834 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. ΠΠ°Π»Π΅Π΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π·Π°Π΄Π°ΡΠ° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ²Π΅ΡΡΠΎΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ (Π‘ΠΠ‘) ΡΠΈΠΏΠ° ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ-Π΄Π΅ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ. ΠΠ»Ρ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈΡΡ ΡΡΠΈ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
ΡΠ΅ΡΠ΅ΠΉ, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ U-Net, SegNet ΠΈ Poly-YOLOv3. ΠΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ΡΡ Poly-YOLOv3, Π΄ΠΎΡΡΠΈΠ³Π½ΡΠ² ΡΠΎΡΠ½ΠΎΡΡΠΈ 0,97 ΠΏΡΠΈ ΡΡΠ΅Π΄Π½Π΅ΠΌ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ½ΠΈΠΌΠΊΠ° 385 Ρ Π²Π΅Π±-ΡΠ΅ΡΠ²ΠΈΡΠΎΠΌ Google Colab. Π’Π°ΠΊΠΆΠ΅ Π±ΡΠ»Π° ΡΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π° Π±Π°Π·Π° Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΈ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΠΌΠΈ ΠΎΠ±Π»Π°ΡΡΡΠΌΠΈ
ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΡΡ ΠΏΠΎ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
Π ΡΡΠ°ΡΡΠ΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
ΡΠ΅ΠΊ, ΠΌΠΎΡΠ΅ΠΉ ΠΈ ΠΎΠΊΠ΅Π°Π½ΠΎΠ² ΠΏΠΎ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΡ Π΄Π°Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
Π½Π° ΡΠ°Π·Π»ΠΈΠ²Ρ Π½Π΅ΡΡΠΈ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
, Π²ΡΠ·Π²Π°Π½Π½ΡΡ
ΡΠ²Π΅ΡΠ΅Π½ΠΈΠ΅ΠΌ Π²ΠΎΠ΄ΠΎΡΠΎΡΠ»Π΅ΠΉ, Π²Π΅ΡΠ΅ΡΡΠ², Π½Π΅ ΠΏΡΠΈΠ½ΠΎΡΡΡΠΈΡ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΠ΅ΡΠ± (Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠ°Π»ΡΠΌΠΎΠ²ΠΎΠ΅ ΠΌΠ°ΡΠ»ΠΎ), Π±Π»ΠΈΠΊΠΎΠ² ΠΏΡΠΈ ΡΡΠ΅ΠΌΠΊΠ΅ ΠΈΠ»ΠΈ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ (ΡΠ°ΠΊ Π½Π°Π·ΡΠ²Π°Π΅ΠΌΡΠ΅ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΈΒ»). ΠΠ½ΠΎΠ³ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² Π΄Π°Π½Π½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΎΡ ΡΠ°Π΄Π°ΡΠΎΠ² Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π°ΠΏΠ΅ΡΡΡΡΠΎΠΉ (Synthetic Aperture Radar (SAR) images), ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ ΡΠΎΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ. ΠΠΎΡΠ»Π΅Π΄ΡΡΡΠ°Ρ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΡΠ±Π°, Π° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΡΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² ΠΊΠ°ΠΊ Π·Π°Π΄Π°ΡΠΈ Π±ΠΈΠ½Π°ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΊΠΎΠ³Π΄Π° ΡΡΠ°Π³ΠΌΠ΅Π½Ρ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π΅ΡΡΡ Ρ ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΠΌΠ΅ΡΠ°ΠΌΠΈ ΠΈΠ· ΠΊΠ»Π°ΡΡΠ° Π½Π΅ΡΡΡΠ½ΡΡ
ΠΏΡΡΠ΅Π½ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
. ΠΡΠ½ΠΎΠ²ΠΎΠΉ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠ»ΡΠΆΠΈΡ ΠΎΠ±Π»Π΅Π³ΡΠ΅Π½Π½Π°Ρ ΡΠ΅ΡΡ VGG16. ΠΡΠΈ ΠΏΡΠ΅Π²ΡΡΠ΅Π½ΠΈΠΈ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π²ΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎ Π½Π°Π»ΠΈΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²Π° Π½Π΅ΡΡΠΈ. ΠΠ»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΡΠΈ Π±ΡΠ» ΡΠΎΠ±ΡΠ°Π½ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ Π½Π°Π±ΠΎΡ Π΄Π°Π½Π½ΡΡ
ΠΈΠ· ΠΎΡΠΊΡΡΡΡΡ
ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅ΡΡΡΡΠΎΠ². Π‘ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΡΠ±Π°Π»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΡΡΡ Π²ΡΠ±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΊΠ»Π°ΡΡΠ°ΠΌ, ΡΡΠΎ ΠΏΠΎΡΡΠ΅Π±ΠΎΠ²Π°Π»ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°ΡΠ³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ²Π΅ΡΠΎΠ²ΡΡ
ΠΌΠ°Π½ΠΈΠΏΡΠ»ΡΡΠΈΡΡ
, Π½ΠΎ ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΎΡΡΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ (Generative Adversarial Network, GAN). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ ΠΈ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΎΠ²Β» Π½Π° ΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ Π΄ΠΎΡΡΠΈΠ³Π°Π΅Ρ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ 0,91 ΠΈ 0,834 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. ΠΠ°Π»Π΅Π΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π·Π°Π΄Π°ΡΠ° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ²Π΅ΡΡΠΎΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ (Π‘ΠΠ‘) ΡΠΈΠΏΠ° ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ-Π΄Π΅ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ. ΠΠ»Ρ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈΡΡ ΡΡΠΈ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
ΡΠ΅ΡΠ΅ΠΉ, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ U-Net, SegNet ΠΈ Poly-YOLOv3. ΠΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ΡΡ Poly-YOLOv3, Π΄ΠΎΡΡΠΈΠ³Π½ΡΠ² ΡΠΎΡΠ½ΠΎΡΡΠΈ 0,97 ΠΏΡΠΈ ΡΡΠ΅Π΄Π½Π΅ΠΌ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ½ΠΈΠΌΠΊΠ° 385 Ρ Π²Π΅Π±-ΡΠ΅ΡΠ²ΠΈΡΠΎΠΌ Google Colab. Π’Π°ΠΊΠΆΠ΅ Π±ΡΠ»Π° ΡΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π° Π±Π°Π·Π° Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΈ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΠΌΠΈ ΠΎΠ±Π»Π°ΡΡΡΠΌΠΈ
Impacts of Oil Spills on Altimeter Waveforms and Radar Backscatter Cross Section
Ocean surface films can damp short capillary-gravity waves, reduce the surface mean square slope, and induce sigma0 blooms in satellite altimeter data. No study has ascertained the effect of such film on altimeter measurements due to lack of film data. The availability of Environmental Response Management Application (ERMA) oil cover, daily oil spill extent, and thickness data acquired during the Deepwater Horizon (DWH) oil spill accident provides a unique opportunity to evaluate the impact of surface film on altimeter data. In this study, the Jason-1/2 passes nearest to the DWH platform are analyzed to understand the waveform distortion caused by the spill as well as the variation of Ο0 as a function of oil thickness, wind speed, and radar band. Jason-1/2 Ku-band Ο0 increased by 10 dB at low wind speed (s-1) in the oil-covered area. The mean Ο0 in Ku and C bands increased by 1.0-3.5 dB for thick oil and 0.9-2.9 dB for thin oil while the waveforms are strongly distorted. As the wind increases up to 6 m s-1, the mean Ο0 bloom and waveform distortion in both Ku and C bands weakened for both thick and thin oil. When wind exceeds 6 m s-1, only does the Ο0 in Ku band slightly increase by 0.2-0.5 dB for thick oil. The study shows that high-resolution altimeter data can certainly help better evaluate the thickness of oil spill, particularly at low wind speeds. Β© 2017. American Geophysical Union
Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach
This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo
On MODIS Retrieval of Oil Spill Spectral Properties in the Marine Environment
With its two daily acquisitions and the possibility to obtain near-real-time data free of charge, the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) shows interest- ing potential as a cost-effective supplementary tool for oil spill monitoring in the marine environment. The mechanism behind MODIS oil feature detection, as well as the type of information that might be retrieved, strictly depends on the illumination condi- tions. In the presence of sunglint contamination, MODIS can just locate the oil spill as a sea surface roughness anomaly, in similarity with radar observations, and no additional spectral information can be retrieved. MODIS detection in the absence of sunglint con- tamination however might allow extraction of oil feature spectral properties, which, in turn, may help in oil discrimination and classification. Careful atmospheric correction must be applied. An example of oil spill spectral property extraction from MODIS images is here shown for the Deepwater Horizon accidental oil spill.JRC.H.1-Water Resource