Abstract
Since the result images obtained by deep semantic segmentation neural networks are usually not perfect, especially at object borders, the conditional random field (CRF) method is frequently utilized in the result post-processing stage to obtain the corrected classification result image. The CRF method has achieved many successes in the field of computer vision, but when it is applied to remote sensing images, overcorrection phenomena may occur. This paper proposes an end-to-end and localized post-processing method (ELP) to correct the result images of high-resolution remote sensing image classification methods. ELP has two advantages. (1) End-to-end evaluation: ELP can identify which locations of the result image are highly suspected of having errors without requiring samples. This characteristic allows ELP to be adapted to an end-to-end classification process. (2) Localization: Based on the suspect areas, ELP limits the CRF analysis and update area to a small range and controls the iteration termination condition. This characteristic avoids the overcorrections caused by the global processing of the CRF. In the experiments, ELP is used to correct the classification results obtained by various deep semantic segmentation neural networks. Compared with traditional methods, the proposed method more effectively corrects the classification result and improves classification accuracy.
Highlights
With the advent of high-resolution satellites and drone technologies, an increasing number of high-resolution remote sensing images have become available, making automated processing technology increasingly important for utilizing these images effectively [1]
The results show that compared with the traditional conditional random field (CRF) method, the proposed method more effectively corrects classification result images and improves classification accuracy
Our experiments demonstrate that when faced with complicated remote sensing images, the CRF algorithm often has difficulty achieving a substantially improved correction effect; without restricting the mechanism by using additional samples, the CRF may overcorrect, leading to a decrease in the classification accuracy
Summary
With the advent of high-resolution satellites and drone technologies, an increasing number of high-resolution remote sensing images have become available, making automated processing technology increasingly important for utilizing these images effectively [1]. Deep learning technology, which can extract higher-level features in complex data, has been widely studied in the high-resolution remote sensing classification field in recent years [4]. In the field of remote sensing classification, the most widely used DSSNN architectures include fully convolutional networks (FCNs), SegNETs, and U-Nets [7,8,9]. In the process of segmenting high-resolution remote sensing images of urban buildings, due to their hierarchical feature extraction structures, DSSNNs can extract buildings’ spatial and spectral building features and achieve better building recognition results [11,12]. Based on U-Nets, FCNs, and transmitting structures that add additional spatial information, DSSNNs can improve road border and centerline recognition accuracy obtained from high-resolution remote sensing images [15]. Through the FCN and SegNET architectures, DSSNNs can obtain deep information regarding land cover areas and can classify complex land cover systems in an automatic and end-to-end manner [16,17,18]
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