6 research outputs found
Whale counting in satellite and aerial images with deep learning
Despite their interest and threat status, the number of whales in world’s oceans remains highly
uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or
through high-resolution images. Since deep convolutional neural networks (CNNs) are achieving great
performance in several computer vision tasks, here we propose a robust and generalizable CNN-based
system for automatically detecting and counting whales in satellite and aerial images based on open
data and tools. In particular, we designed a two-step whale counting approach, where the first CNN
finds the input images with whale presence, and the second CNN locates and counts each whale in those
images. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a
performance (F1-measure) of 81% in detecting and 94% in counting whales. Combining these two steps
increased accuracy by 36% compared to a baseline detection model alone. Applying this cost-effective
method worldwide could contribute to the assessment of whale populations to guide conservation
actions. Free and global access to high-resolution imagery for conservation purposes would boost this
process.S.T. was supported by the Ramón y Cajal Programme of the Spanish government (RYC-2015-18136). S.T., E.G.,
and F.H. were supported by the Spanish Ministry of Science under the project TIN2017-89517-P. D. A-S. received
support from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, and from ERDF and Andalusian
Government under the project GLOCHARID. D.A.-S. received support from NASA Work Programme on Group
on Earth Observations - Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446, from
project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Programme under
grant agreement No. 641762, and from the Spanish Ministry of Science under project CGL2014-61610-EXP
and grant JC2015-00316. M.R. received support from International mobility grant for prestigious researchers by
(CEIMAR) International Campus of Excellence of the Sea
Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network
Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure