A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification. Issue 23 (2nd December 2017)

• Record Type: Journal Article
• Authors: Pan, Xin; Zhao, Jian
• Is Part Of: International journal of remote sensing. Volume 38:Issue 23(2017)
• Page Start: 6554
• Page End: 6581
• Publication Date: 2017-12-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1362131
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: As one of the most important algorithms in the field of deep learning technology, the convolutional neural network (CNN) has been successfully applied in many fields. CNNs can recognize objects in an image by considering morphology and structure rather than simply individual pixels. One advantage of CNNs is that they exhibit translational invariance; when an image contains a certain degree of distortion or shift, a CNN can still recognize the object in the image. However, this advantage becomes a disadvantage when CNNs are applied to pixel-based classification of remote-sensing images, because their translational invariance characteristics causes distortions in land-cover boundaries and outlines in the classification result image. This problem severely limits the application of CNNs in remote-sensing classification. To solve this problem, we propose a central-point-enhanced convolutional neural network (CE-CNN) to classify high-resolution remote-sensing images. By introducing the central-point-enhanced layer when classifying a sample, the CE-CNN increases the weight of the central point in feather maps while preserving the original textures and characteristics. In our experiment, we selected four representative positions on a high-resolution remote-sensing image to test the classification ability of the proposed method and compared the CE-CNN with the traditional multi-layer perceptron (MLP) and a traditional CNN. The results show that the proposed method can not only achieves a higher classification accuracy but also less distortion and fewer incorrect results at the boundaries of land covers. We further compared the CE-CNN with six state-of-the-art methods: k -NN, maximum likelihood, classification and regression tree (CART), MLP, support vector machine, and CNN. The results show that the CE-CNN's classification accuracy is better than the other methods.