![]() These maps are then collapsed into one by taking the argmax at each pixel position. To generate this, our network has a one-hot-encoded output channel for each of the possible class labels: Below is an example of what the architecture for an image classification task might look like:Īs we can see, the output of the network is a single prediction for a class label, but what would the output be for a segmentation task, in which an image may contain objects of multiple classes in different locations? Well, in such a case, we want our network to produce a pixel-wise map of classifications like the following:Īn image and its corresponding simplified segmentation map of pixel class labels. It’s largely for this reason that filters typically decrease in size over the course of a network, while also increasing in number such that fine-grained features can be learned. However, given that each filter is represented by a set of weights to be learned, having lots of filters of the same size as the original input image makes training a model quite computationally expensive. The more filters a network has-or the deeper a network is-the more features it can extract from an image and thus the more complex patterns it can learn for the purpose of informing its final classification decision. Each filter has different weights and thus gets trained to recognize a particular feature of an image. convolving) rectangular “filters” over an image. Let’s take a look at how CNNs work for classification before getting into the more complex task of segmentation.Īs you may know, CNNs work by sliding (i.e. You’re likely familiar with CNNs and their association with computer vision tasks, particularly with image classification. I’ll be exploring approaches taken to the SpaceNet 6 challenge later in the post, but first, let’s explore a few of the fundamental building blocks of machine learning techniques for image segmentation to uncover how code can be used to detect objects in this way. Given this, the challenge provides us with a good starting point from which we can begin to build understanding of what is an inherently advanced process. The SpaceNet project’s SpaceNet 6 challenge, which ran from March through May 2020, was centered on using machine learning techniques to extract building footprints from satellite images-a fairly straightforward problem statement for an image segmentation task. Applications of this type of aerial imagery labeling are widespread, from analyzing traffic to monitoring environmental changes taking place due to global warming. ![]() In the case of satellite imagery, these objects may be buildings, roads, cars, or trees, for example. Additionally, segmentation differs from object detection in that it works at the pixel level to determine the contours of objects within an image. What is image segmentation?Īs opposed to image classification, in which an entire image is classified according to a label, image segmentation involves detecting and classifying individual objects within the image. In this post, I’ll be discussing image segmentation techniques for satellite data and using a pre-trained neural network from the SpaceNet 6 challenge to test an implementation out myself. In my first blog, I walked through the process of acquiring and doing basic change analysis on satellite data. ![]()
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