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Benefiting from the great success of deep learning in computer vision,CNN-based object detection methods have drawn significant attentions. Variousframeworks have been proposed which show awesome and robust performance for alarge range of datasets. However, for building detection in remote sensingimages, buildings always pose a diversity of orientations which makes it achallenge for the application of off-the-shelf methods to building detection.In this work, we aim to integrate orientation regression into the popularaxis-aligned bounding-box detection method to tackle this problem. To adapt theaxis-aligned bounding boxes to arbitrarily orientated ones, we also develop analgorithm to estimate the Intersection over Union (IoU) overlap between any twoarbitrarily oriented boxes which is convenient to implement in GraphicsProcessing Unit (GPU) for accelerating computation. The proposed methodutilizes CNN for both robust feature extraction and rotated bounding boxregression. We present our modelin an end-to-end fashion making it easy totrain. The model is formulated and trained to predict orientation, location andextent simultaneously obtaining tighter bounding box and hence, higher meanaverage precision (mAP). Experiments on remote sensing images of differentscales shows a promising performance over the conventional one.

Learning Orientation-Estimation Convolutional Neural Network for Building Detection in Optical Remote Sensing Image