Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
We present an end to end trainable deep convolutional neural network (DCNN) for semantic segmentation with built in awareness of semantically meaningful boundaries. Semantic segmentation is a fundamental remote sensing task, and most state of the art methods rely on DCNNs as their workhorse. A major reason for their success is that deep networks learn to accumulate contextual information over very large windows (receptive fields) However, this success comes at a cost, since the associated loss of effecive spatial resolution washes out high frequency details and leads to blurry object boundaries. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model, First, we construct a comparatively simple, memory efficient model by adding boundary detection to the Segnet encoder decoder architecture. Second, we also include boundary detection in FCN type models and set up a high end classifier ensemble. We show that boundary detection significantly improves semantic segmentation with CNNs. Our high end ensemble achieves 90% overall accuracy on the ISPRS Vaihingen benchmark.
ArXiv