ERCIM News 133

We present a method combining marked point processes and convolutional neural networks applied to the detection of small objects in optical satellite images. In such images, objects are densely scattered, and visual information is scarce. The point process framework allows factoring in priors to account for object interactions. Classical point process approaches make use of contrast measures to account for object location. These fail when contrast is low and visual aspect is varied. We replace those with terms build from convolutional neural network outputs. Moreover, we propose a method to learn the parameters of the point process energy model. We show our approach improves results from the straight convolutional neural network outputs. The code will be available at github.com/Ayana-Inria/

Learning point process models for vehicles detection using CNNs in satellite images

In this article we present a combination of marked point processes with convolutional neural networks applied to remote sensing. While point processes allow modeling interactions between objects via priors, classical methods rely on contrast measures that become unreliable as objects of interest and context become more diverse. We propose learning likelihood measures using convolutional neural networks to make these measures more versatile and resilient. We apply our method to the detection of vehicles in satellite images.

/pdf/point-process-and-cnn-for-small-object-detection-in-v0no6dlg.pdf

Point process and CNN for small object detection in satellite images

Convolutional neural networks (CNN) have shown great results for object-detection tasks by learning texture and pattern-extraction filters. However, object-level interactions are harder to grasp without increasing the complexity of the architectures. On the other hand, Point Process models propose to solve the detection of the configuration of objects as a whole, allowing the factoring in of the image data and the objects’ prior interactions. In this paper, we propose combining the information extracted by a CNN with priors on objects within a Markov Marked Point Process framework. We also propose a method to learn the parameters of this Energy-Based Model. We apply this model to the detection of small vehicles in optical satellite imagery, where the image information needs to be complemented with object interaction priors because of noise and small object sizes.

Learning Point Processes and Convolutional Neural Networks for Object Detection in Satellite Images

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Point process and CNN for small object detection in satellite images

Learning Point Processes and Convolutional Neural Networks for Object Detection in Satellite Images

Learning point process models for vehicles detection using CNNs in satellite images