Abstract
In this paper, a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for hyperspectral image (HSI) change detection (CD) is proposed. First, tensors are extracted in two HSIs recorded at different time points separately and tensor pairs are constructed. The tensor pairs are then incorporated into the spectral-spatial network to obtain two spectral-spatial vectors. Thereafter, the Euclidean distances of the two spectral-spatial vectors are calculated to represent the similarity of the tensor pairs. We use a Siamese network based on contrastive loss to train and optimize the network so that the Euclidean distance output by the network describes the similarity of tensor pairs as accurately as possible. Finally, the values obtained by inputting all tensor pairs into the trained model are used to judge whether a pixel belongs to the change area. SSCNN-S aims to transform the problem of HSI CD into a problem of similarity measurement for tensor pairs by introducing the Siamese network. The network used to extract tensor features in SSCNN-S combines spectral and spatial information to reduce the impact of noise on CD. Additionally, a useful four-test scoring method is proposed to improve the experimental efficiency instead of taking the mean value from multiple measurements. Experiments on real data sets have demonstrated the validity of the SSCNN-S method.
Highlights
Due to the development of remote sensing technology it is possible to obtain hyperspectral images (HSIs) of the same area at different time points
Experiments using three real data sets show that the spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) method proposed in this paper shows good performance in solving the problem of change detection (CD) in HSIs
To verify the effect of SSCNN-S on CD, we first introduce three real hyperspectral data sets used for experiments and provide indexes for evaluating the effects of different algorithms
Summary
Due to the development of remote sensing technology it is possible to obtain hyperspectral images (HSIs) of the same area at different time points. Using multitemporal remote sensing data has an important application value in disaster assessment [1], terrain change analysis [2], urban change analysis [3] and resource auditing. CD based on time series data [12,13]. Change vector analysis (CVA) [14] is often combined with other methods. The change area is determined by calculating the values and their weights of MAD variables.
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