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 achhieves > 90% overall accuracy on the ISPRS Vaihingen bechmark.
An alternative strategy to better retain boundaries between objects in the classification results is to take into account semantically meaningful boundaries, e.g. by including an explicit object boundary detector in the SegNet encoder-decoder architecture or in FCN-type models REF .
Classification With an Edge: Improving Semantic Image Segmentation with Boundary Detection
ISPRS Journal of Photogrammetry and Remote Sensing, Volume 135, January 2018, Pages 158-172, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2017.11.009
Mathematics Computer Science