Abstract
Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC–FCN) model is proposed to perform end-to-end building segmentation. Our MC–FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km 2 and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC–FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U–Net model, MC–FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC–FCN.
Highlights
Due to the frequent changing of landmarks, especially for rapidly developing cities, it is essential to be able to immediately update such changes for the purposes of navigation and urban planning
The main contributions of this study are summarized as follows: (1) We propose a novel multi-constraint fully convolutional architecture that increases the performance of the state-of-the-art method (i.e., U–Net) in building segmentation of very-high-resolution aerial imagery; and (2) we further analyze the effects of different combinations of constraints in MC–fully convolutional networks (FCNs) to explore how these constraints affect the performance of the deep convolutional neural networks (CNNs) models
In the left-middle corner of Test-1, where FCN, U–Net and multi-constraint fully convolutional network (MC–FCN) misclassifies a small lake, histogram of oriented gradients (HOG)–Ada is still able to distinguish the lake with the help of textual features
Summary
Due to the frequent changing of landmarks, especially for rapidly developing cities, it is essential to be able to immediately update such changes for the purposes of navigation and urban planning. A great amount of image segmentation algorithms have been proposed. The majority of these algorithms can be classified into four categories: threshold-based, edge-based, region-based and classification-based methods. Image thresholding is not capable of differentiating among different regions with similar grayscale values. Region-based methods segment different parts of an image through clustering [6,7,8,9], region-growing [10] or shape analysis [11,12]. Due to the variety of illuminance and texture conditions of an image, edge-based or region-based methods cannot provide stable and generalized results. Unlike the other three methods, classification-based methods treat image segmentation as a process of classifying the category of every pixel [13]. Since the segmentation is made by classifying every pixel, the classification-based method can produce more precise segmentations with proper feature extractors and classifiers
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