Fully Connected Conditional Radom Fields for High Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks

Bin ZhangYueyan Liu


The interpretation of land use/land cover (LULC) is a hotspot and difficult issue in the field of high resolution remote sensing image processing as well as land resource management. Training a new (or existing) Convolutional Neural Networks (CNNs) architecture fully for LULC classification needs a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNNs for LULC is acceptable. To improve the classification accuracy for high resolution remote sensing images, it is necessary to utilize some hand-crafted features and adopt a classifier for post-processing. A Fully Connected Conditional Radom Fields (FC-CRFs), to utilize the fine-tuned CNNs layers, hand-crafted features and fully connected pairwise potentials, is proposed for image classification of high resolution remote sensing images. First, an existing CNNs model is adopted, and the parameters of CNNs are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type also could be calculated. Second, we consider the hand-craft features, combined with Support Vector Machine (SVM) classifier, the probabilities belong to each LULC class type are achieved. Combined with the probabilities achieved by fine-tuned CNNs, new featur...Continue Reading

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