In this paper, we explore the use of two machine learning algorithms: (a) random forest for structured labels and (b) fully convolutional neural network for the land cover classification of multi-sensor remote sensed images. In random forest algorithm, individual decision trees are trained on features obtained from image patches and corresponding patch labels. Structural information present in the image patches improves the classification performance when compared to just utilizing pixel features. Random forest method was trained and evaluated on the ISPRS Vaihingen dataset that consist of true ortho photo (TOP: near IR, R, G) and Digital Surface Model (DSM) data. The method achieves an overall accuracy of 86.3% on the test dataset. We also show qualitative results on a SAR image. In addition, we employ a fully convolutional neural network framework (FCN) to do pixel-wise classification of the above multi-sensor image. TOP and DSM data have individual convolutional layers with features fused before the fully convolutional layers. The network when evaluated on the Vaihingen dataset achieves an overall classification accuracy of 88%.
S. Piramanayagam, W. Schwartzkopf, F. W. Koehler, and E. Saber, "Classification of remote sensed images using random forests and deep learning framework," Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040L (Presented at SPIE Remote Sensing: September 27, 2016; Published: 18 October 2016); https://doi.org/10.1117/12.2243169.
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