Alteration Detection of Multispectral/Hyperspectral Images Using Dual-Path Partial Recurrent Networks

Abstract

Numerous alteration detection methods are designed based on image transformation algorithms and divergence of bi-temporal images. In the process of feature transformation, pseudo variant information caused by complex external factors will be highlighted. As a result, the error of divergence between the two images will be further enhanced. In this paper, we propose to fuse the variability of Deep Neural Networks’ (DNNs) structure flexibly with various detection algorithms for bi-temporal multispectral/hyperspectral imagery alteration detection. Specifically, the novel Dual-path Partial Recurrent Networks (D-PRNs) was proposed to project more accurate and effective deep features. The Unsupervised Slow Feature Analysis (USFA), Iteratively Reweighted Multivariate Alteration Detection (IRMAD), and Principal Component Analysis (PCA) were then utilized, respectively, with the proposed D-PRNs, to generate two groups of transformed features corresponding to the bi-temporal remote sensing images. We next employed the Chi-square distance to compute the divergence between two groups of transformed features and, thus, obtain the Alteration Intensity Map. Finally, threshold algorithms K-means and Otsu were, respectively, applied to transform the Alteration Intensity Map into Binary Alteration Map. Experiments were conducted on two bi-temporal remote sensing image datasets, and the testing results proved that the proposed alteration detection model using D-PRNs outperformed the state-of-the-art alteration detection model.

CD-SDN: Unsupervised Sensitivity Disparity Networks for Hyper-Spectral Image Change Detection

“…Statistical analysis has confirmed the followings. (1) CD-USNet [21] always outperforms CD-CSNet in UN_CHG coefficient, but underperforms in OA_CHG, this is the basis to establish our SDN; (2) deep learning-based frameworks (e.g., DSFA, CD-USNet, CD-CSNet) with linear slow feature analysis (L-SFA) do not always outperform the model (CD-FCN) without L-SFA in any coefficients. Nevertheless, L-SFA is definitely a positive for the improvement of comprehensive coefficients; (3) although two branches of the proposed schema, CD-SDN-AM and CD-SDN-AL, do not always perform the best in one-sided coefficients OA_CHG and OA_UN, they occupy the top two in comprehensive coefficients OA, Kappa, and F1.…”

“…To highlight the priority of the proposed CD-SDN, we compare it with the state-ofthe-art works: CVA [8], CD-FCN [20], DSFA [20], CD-USNet which we proposed in our previous work and was also known in [21], and the proposed CD-CSNet. Note that in our experiment, we adopt 5 × 10 −5 as the learning rate (LR) for FCN, DSFA, USNet, and CSNet.…”

“…The observations revealed that the DNN-based models CD-FCN [20], DSFA [20], CD-USNet [21], CD-CSNet, CD-SDN-AM, and CD-SDN-AL, are much good at eliminating noise interruption, they all achieve overwhelming superiority over the linear algebraic algorithm CVA in comprehensive coefficients OA, Kappa and F1, this could be reflected in HSM with a small area of false marks (FN and TN); particularly, the proposed CD-SDN-AL and CD-SDN-AM gain the smallest area of false detection.…”

“…Accordingly, in this paper, we proposed the SDN, consisting of a USNet and a CSNet, to serve our proposal. Of which, according to the requirement of SDN, the PRN [21] in our last work is employed as USNet; CSNet was first proposed from the fine-tuning of FCN used in model DSFA, it has better sensitivity to changed pixels, compared with USNet. It should be noted that CD-FCN, CD-USNet, and CD-CSNet are models based on deep USNet and CSNet; although SDN contains both USNet and CSNet, this paper mainly proposes the schema CD-SDN, while the models CD-USNet and CD-CSNet with USNet and CSNet are regarded as comparison methods.…”

MV-CDN: Multi-Visual Collaborative Deep Network for Change Detection of Double-Temporal Hyperspectral Images

“…As collaborative network members, the fully connected network (FCNet) is applied in the state-of-the-art model deep slow feature analysis (DSFA) [25]. With SFA, it demonstrates the effectiveness and practicality for change detection in hyperspectral images; the sensitivity disparity networks (SDN) [26], which consist of an unchanged sensitivity network (USNet) we have proposed in our previous work [27] and is also known as dual-path partial recurrent networks (D-PRNs); and a changed sensitivity network (CSNet). The special note is that the SDN members, USNet and CSNet, result from the fine-tuning of FCNet and extensive testing has proven that they are more sensitive to unchanged pixels and more sensitive to changed pixels, respectively.…”