Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images

Abstract

Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has better change detection performance. However, changes of multi-temporal images are usually complex, existing methods are not effective enough. In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection. Therefore, in this paper, based on deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called Deep Slow Feature Analysis (DSFA). In DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The CVA pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world datasets and a public hyperspectral dataset. The visual comparison and quantitative evaluation have both shown that DSFA could outperform the other state-of-theart algorithms, including other SFA-based and deep learning methods.

Adaptive CNN Ensemble for Complex Multispectral Image Analysis

“…Besides, we also performed the training for hyperparameter tuning, for example, change in EPOC, batch size, and learning rate. Finally, we found twenty (20) layered CNN architecture most appropriate, as shown in Figure 2. e optimized CNN model possesses twenty (20) layers, fourteen (14) layers for the feature extraction, and six (6) layers for classification.…”

“…Finally, we found twenty (20) layered CNN architecture most appropriate, as shown in Figure 2. e optimized CNN model possesses twenty (20) layers, fourteen (14) layers for the feature extraction, and six (6) layers for classification. e optimized CNN model uses six (6) different types of layers with tuned parameters, such as (1) batch-normalization layer, (2) convolutional layer, (3) Gaussian noise layer, (4) pooling layer, (5) dropout layer, and (6) dense layer.…”

“…is makes the supervised machine learning ineffective. A recent study has proposed an unsupervised approach, called Deep SFA (DSFA) [20]. DSFA is based on slow feature analysis theory and deep network to detect the change for multitemporal remotes sensing images.…”

Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images

“…In [32], Du et al present a slow feature analysis (SFA) theory based deep neural network for optical remote sensing change detection. This network non-linearly maps the input bi-temporal data into a higher dimensional space, as shown in Figure 1b.…”

“…Changed pixels are finally identified by an unsupervised K-Means clustering method [31]. Note that a similar idea has been proposed in [32], in which the slow…”