Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Ahram Song,Yongil Kim

doi:10.3390/rs12071099

Abstract

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

Highlights

Change detection (CD) is the process of identifying changes in land cover or land use in the same geographical area over time [1]
To evaluate the effectiveness of the proposed CD methods, a comparison was made between the CD results obtained from intermediate steps, which were the output of the CD network when using label data generated from SIDSCA, ground truth data, and pre-trained information in the source labeled dataset without additional training
As we can see from the CIR images, vegetation was apparent to some extent and the difference images (DIs) from SIDSCA were not recognized as changed areas from vegetation to bare soil (Figure 8a,d) This is because SIDSCA determines changes based on spectral similarity

Summary

Introduction

Change detection (CD) is the process of identifying changes in land cover or land use in the same geographical area over time [1]. CD is one of the most important fields in remote sensing (RS) because it can be used with RS images in many real-world applications, such as the measurement of urban expansion [2], disaster evaluation [3], and crop monitoring [4]. As the availability of images from satellites and unmanned aerial vehicles (UAVs) with very-high resolution (VHR) cameras has increased, a large amount of data with a resolution of less than 1 m has been collated on regions of interest. Smaller and lighter hyperspectral sensors have been developed that can be integrated with UAVs and provide hundreds of spectral bands. Hyperspectral UAV images can provide high levels of spatial detail and rich spectral information about surface materials [5].

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Conclusion