Abstract
Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.
Highlights
Remote sensing images with very high resolution (VHR) are widely used in many applications including land cover mapping and monitoring [1], multi-angle urban classification analysis [2], automatic road detection [3], as well as the identification of tree species in forest management [4]
Semantic segmentation of remote sensing imagery aims to classify every pixel into a given category, and it is an important task for understanding and inferring objects [9,10] and the relationships between spatial objects in a scene [11]
The ISPRS 2D semantic labelling VHR remote sensing imageries of urban districts are used in the experiments, including the Vaihingen (Germany) and Potsdam (Germany) datasets, as these are open asset datasets provided online
Summary
Remote sensing images with very high resolution (VHR) are widely used in many applications including land cover mapping and monitoring [1], multi-angle urban classification analysis [2], automatic road detection [3], as well as the identification of tree species in forest management [4]. Different objects may present the same spectral values within the remote sensing imagery, which make it more difficult to extract reasonable spatial features to resolve the classification of pixels in extracting the buildings. Recent research makes an effort to improve the accuracy in areas such as encoding of images, extraction of features from raw images [38,39], and the use of deep neural networks such as CNNs, FCNs, and so on, to label pixels, especially for the VHR remote sensing imagery [40,41]. Within the remote sensing imagery and their corresponding normalized digital surface model, hand-crafted features such as NDVI, PCA1 as well as the classified segmentation maps are regarded as the inputs to train the network. I=1 k=1 where xi, yi m i=1 is assumed to be the training data, xi represents the vectored features, and yi is the labeled data, m represents the number of samples, and w is a weight map in the network to be optimized
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