Abstract
As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.
Highlights
Classification is a fundamental task for remote sensing imagery analysis
The rough class lower part of Figure 1), the trained Fully Convolutional Network (FCN) network is performed on an input image to generate a prediction, the input thenprediction, input into with the Conditional Random Fields (CRFs) post-processing module generate the rough class with prediction
This paper presents a classification approach for high resolution images using an improved
Summary
Classification is a fundamental task for remote sensing imagery analysis Applying intelligent methods, such as pattern recognition and statistical learning, is an effective way to obtain class information of ground objects. Classification was mainly for low spatial resolution (10–30 m) images and pixel-leveled images, including unsupervised classification ( known as clustering, such as K-means [1]) and supervised classification (such as Neural Networks [2,3] and Support Vector Machines [4,5]) These methods often use only spectral information of the images, and have formed general modules in commercial software, and have been successfully applied in land resources, environment, agriculture, and other fields. Yuan Yuan et al [6] and Qi Wang et al [7] applied the latest achievements in the machine learning field, such as Manifold Ranking and Sparse Representation, to hyperspectral image classification
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