=Paper=
{{Paper
|id=Vol-2670/MediaEval_19_paper_64
|storemode=property
|title=MediaEval2019: Flood Detection in Time Sequence Satellite Images
|pdfUrl=https://ceur-ws.org/Vol-2670/MediaEval_19_paper_64.pdf
|volume=Vol-2670
|authors=Pallavi Jain,Bianca Schoen-Phelan,Robert Ross
|dblpUrl=https://dblp.org/rec/conf/mediaeval/0003SR19
}}
==MediaEval2019: Flood Detection in Time Sequence Satellite Images==
https://ceur-ws.org/Vol-2670/MediaEval_19_paper_64.pdf
MediaEval2019: Flood Detection in Time Sequence Satellite
Images
Pallavi Jain, Bianca Schoen-Phelan, Robert Ross
Technological University Dublin
Dublin, Ireland
{pallavi.jain,bianca.schoenphelan,robert.ross}@tudublin.ie
ABSTRACT NDWI struggles to separate built up areas from water bodies, as
In this work, we present a flood detection technique from time series NDWI and built-up falls in same range [20] of reflectance values.
satellite images for the City-centered satellite sequences (CCSS) task Considering the built-up area issue and incapability of shallow
in the MediaEval 2019 competition [1]. This work utilises a three water detection using NDWI, a combination of two indices has
channel feature indexing technique [13] along with a VGG16 pre- been proposed [15]. The combination of the NDWI water index
trained model for automatic detection of floods. We also compared along with an index using Blue and NIR bands to highlight shallow
our result with RGB images and a modified NDWI technique by water along with water bodies. Similarly, Li et al.,2017 [13] proposed
Mishra et al, 2015 [15]. The result shows that the three channel a three channel feature index for supervised learning. In this work
feature indexing technique performed the best with VGG16 and they leveraged the three indexes being NDVI, NDWI, and RE-NDWI
is a promising approach to detect floods from time series satellite and combined them to create 3 channel images instead of one like
images. RGB [10]. All these indexing techniques are capable of mapping
water bodies. Consequently, we assume that these can also be useful
in flood water mapping. This could be helpful to rescue teams and
1 INTRODUCTION provide an improved understanding of disaster situations and areas.
Flooding is the most common natural disaster event, which affects As these processes are mostly manual, automating them can be
people every year all around the world. In most cases, it directly hugely helpful in order to have accurate information in a timely
impacts human life and damages properties. In recent years, many manner.
techniques have been developed to organise rescue operations in Lately, Deep Convolutional Neural Networks (CNNs) such as
such events in more efficient ways. Flood mapping through satellite AlexNet [11], VGG16 [17], have performed very well in many do-
images is one such area where a lot of research has been conducted mains such as speech recognition, image classification and natural
aiming to monitor floods and perform timely risk analysis [2, 3, 5, language processing. Remote sensing has also become a widely
18]. popular area where deep CNNs have shown good performance [16].
Sentinel-2 provides high resolution multi-spectral images, with However, in order to train the CNN models with a large number
13 bands for emergency services, which can also be useful to moni- of layers, a significant amount of data is required. This is one of
tor and analyse the flooding situation. Each of these bands high- the main challenges in the domain of flood detection. At the same
lights a certain geological features like water, land or clouds. Each time it has been shown that transfer learning or pre-trained deep
band offers a different reflectance and absorbance property which CNNs can be a strong option for automating flood detection [8].
can be exploited for flood detection and monitoring. Among the deep CNNs, VGG16 has shown great performance pre-
Among the 12 bands, visible range bands Red, Green and Blue viously in many image classification tasks like object detection,
create a true colour image. These images can map floods and stand- image segmentation and scene classification [7].
ing water but often suffer from cloud or building shadows which Flood water is mostly a shallow water body, and difficult to detect
prevents accurate mapping. For that reason several water index due to built-up area or cloud shadows. In this work we propose
techniques have been proposed in order to reduce the effects of that if each type of feature such as vegetation, water or clouds are
shadows and expose appropriate water values. The near infrared separated efficiently, it can be trained using a pre-trained deep CNN,
(NIR) band highly absorbs water reflectance and reflects vegeta- which is capable to automate the process of flood detection in time
tion. This property of NIR has made it a popular choice in the past series satellite imagery.
in order to extract water bodies from images. For that reason the
normalised difference water index (NDWI) was introduced [14], 2 APPROACH
which leverages NIR and the green band as shown in equation 1.
NDWI maximises water features and minimises all other features. 2.1 Image Processing
Leveraging this particular water indexing technique resulted in the 2.1.1 Run 1. As shallow water is difficult to map in remote
development of many improvements in recent years [6, 20]. sensing images due to built-up areas, a combination of water index
Green − N IR techniques has been proposed in the past [15]. In this approach
N DW I = (1) NDWI is used along with Blue and NIR band indexing as shown in
Green + N IR
equation 2
Copyright 2019 for this paper by its authors. Use
permitted under Creative Commons License Attribution Green − N IR Blue − N IR
4.0 International (CC BY 4.0). ModN DW I = + (2)
Green + N IR Blue + N IR
MediaEval’19, 27-29 October 2019, Sophia Antipolis, France
MediaEval’19, 27-29 October 2019, Sophia Antipolis, France Jain et al.
2.1.2 Run 2. For this run we used true colour images, that is
three channel RGB composite images with Red, Green and Blue
bands.
2.1.3 Run 3. For this run we leveraged the three-channel index
feature space approach [13]. The images are processed to NDVI [eq.
3] that uses NIR and Red bands, NDWI [eq. 1], and Red Edge NDWI
(RE-NDWI) [eq. 4], which uses green and red edge (RE) vegetation
band. All three of them are then combined horizontally to create
a three-channel images like RGB. This approach highlights the
individual properties of vegetation, water and clouds. Figure 1: Model Architecture
Red − N IR
N DV I = (3)
Red + N IR loss function. The model is trained for approx. 30 epochs depending
Green − RE on best performance of each processed images.
RE_N DW I = (4)
Green + RE
3 RESULTS
2.2 Model For the evaluation of the model we used micro average F1 Score,
The VGG16 network is one of the most popular deep CNN’s for as mentioned in the competition evaluation task [1]. Also, image
image classification and object detection [7, 8, 12]. It consists of 13 data had imbalance classes, due to which accuracy measure can be
convolutional layers and 3 fully-connected layers. We leveraged misleading, for that reason F1 score is an appropriate evaluation
the pre-trained VGG16 network, which is trained on the ImageNet metric as it provides balance score of precision and recall.
dataset [4]. Initial layers only extract the general features, and task The result shown in table 1, which clearly show that the averag-
specific features are extracted by the later layers. We froze the initial ing of images can provide good performance in order to detect if
4 blocks and leveraged the last block for our task. a city is flooded. Additionally, the 3-dimensional feature indexing
technique outperforms the true colour RGB and Mod-NDWI [15]
2.3 Experiment by approximately 3% in both development and test results.
The 12 band data was provided by MediaEval 2019 under subtask
City-centered satellite sequences (CCSS) of the multimedia satellite Table 1: Development and Test Results
task [1]. It consists of 267 sets of sequences in the development
dataset and 68 sets in the test dataset. For the training and testing Run Dev F1 Test F1
of the model we split the development dataset into 80% training set, Run 1 0.963 0.897
10% validation set, and another 10% development test set. Data had Run 2 0.963 0.941
imbalance class, so we used stratified sampling by class to split the Run 3 1.00 0.970
data into train, test, and validation datasets. We also used an image
augmentation technique for training datasets by shifting, rotating
and flipping the images and achieved the boost of approximately
4 CONCLUSION
2-4%.
VGG16 was originally trained to work on 3-channel image data In this work, we explored the automatic detection of floods in an
like RGB. However, Mod-NDWI creates a single-channel images area for sequence of time series images. We used a pixel based
like greyscale, which we consequently converted into a 3-channel averaging approach on RGB, Modified NDWI and a three-channel
image assuming identical values for each input channel. feature indexing technique along with deep CNNs model VGG16.
For processing the time series image, we used a pixel based The results pointed towards significant improvements in flood de-
technique. For that, we created the average image of each set of tection when using a three-channel feature index. Furthermore, it
sequence images after individual image processing and fed those to appears that the averaging technique is efficient in detection of
the VGG16 model. The average image modifies only the changed flood in the city over the time period.
values due to change in image while keeping unchanged values the
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