Abstract
Abstract. Remote sensing earth observation images have a wide range of applications in areas like urban planning, agriculture, environment monitoring, etc. While the industrial world benefits from availability of high resolution earth observation images since recent years, interpreting such images has become more challenging than ever. Among many machine learning based methods that have worked out successfully in remote sensing scene classification, spatial pyramid matching using sparse coding (ScSPM) is a classical model that has achieved promising classification accuracy on many benchmark data sets. ScSPM is a three-stage algorithm, composed of dictionary learning, sparse representation and classification. It is generally believed that in the dictionary learning stage, although unsupervised, one should use the same data set as classification stage to get good results. However, recent studies in transfer learning suggest that it might be a better strategy to train the dictionary on a larger data set different from the one to classify.In our work, we propose an algorithm that combines ScSPM with self-taught learning, a transfer learning framework that trains a dictionary on an unlabeled data set and uses it for multiple classification tasks. In the experiments, we learn the dictionary on Caltech-101 data set, and classify two remote sensing scene image data sets: UC Merced LandUse data set and Changping data set. Experimental results show that the classification accuracy of proposed method is compatible to that of ScSPM. Our work thus provides a new way to reduce resource cost in learning a remote sensing scene image classifier.
Highlights
Remote sensing plays an important role in earth observation, and in this area remote sensing image scene classification is one fundamental problem (Cheng et al, 2017)
ScSPM uses dictionary learning with sparse coding to train a dictionary that captures salient features, low-level features are encoded by the dictionary and represented in a spatial pyramid way to form higher level features, which are used as input to the classifier
We propose a self-taught learning framework using spatial pyramid matching (S-ScSPM) on remote sensing scene classification from high resolution imagery
Summary
Remote sensing plays an important role in earth observation, and in this area remote sensing image scene classification is one fundamental problem (Cheng et al, 2017). With the increasing development of remote sensing imaging techniques, huge amounts of high spatial resolution images have been acquired Detailed contents in these images, make automatic classification a challenging task. The learning objective function is generally difficult to optimize, and what’s more, if ScSPM is applied to classify some data set B, the dictionary should be trained on B. This way of dictionary learning is sometimes called ”task- specific”. We propose a self-taught learning framework using spatial pyramid matching (S-ScSPM) on remote sensing scene classification from high resolution imagery. While the overall classification accuracy using S-ScSPM is compatible to and sometimes outperform that of original ScSPM on labeled data sets, in S-ScSPM the dictionary is learned only on one unlabeled data set, and the resource cost of learning is significantly reduced
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