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Title: An Efficient Deep Representation Based Framework for Large-Scale Terrain Classification
In this paper, we present a novel terrain classifica- tion framework for large-scale remote sensing images. A well- performing multi-scale superpixel tessellation based segmentation approach is employed to generate homogeneous and irregularly shaped regions, and a transfer learning technique is sequentially deployed to derive representative deep features by utilizing suc- cessful pre-trained convolutional neural network (CNN) models. This design is aimed to overcome the big problem of lacking available ground-truth data and to increase the generalization power of the multi-pixel descriptor. In the subsequent classification step, we train a fast and robust support vector machine (SVM) to assign the pixel-level labels. Its maximum-margin property can be easily combined with a graph Laplacian propagation approach. Moreover, we analyze the advantages of applying a feature selection technique to the deep CNN features which are extracted by transfer learning. In the experiments, we evaluate the whole framework based on different geographical types. Compared with other region-based classification methods, the results show that our framework can obtain state-of-the-art performance w.r.t. both classification accuracy and computational efficiency.  more » « less
Award ID(s):
1743050
PAR ID:
10088684
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
International Conference on Pattern Recognition (ICPR)
Page Range / eLocation ID:
940 to 945
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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