Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

Analysis on ecological status and spatial-temporal variation of Tamarix chinensis forest based on spectral characteristics and remote sensing vegetation indices

The reserve of Tamarix forest, located in Changyi, China, is the only national marine special reserve taking Tamarix as the main object of protection. Compared with conventional monitoring technology, remote sensing technology can more comprehensively reflect the ecological environment status and spatial-temporal variation of monitoring objects. Based on spectral characteristics and remote sensing vegetation indices, the ecological status and spatial-temporal variation of Tamarix chinensis forest in the reserve deserve further exploration. Therefore, spectral characteristic, typical vegetation indices, comprehensive health index, VFC, and REP were analyzed based on Sentinel-2A images. Spatial-temporal variation analysis during 2014 to 2018 was analyzed based on GF-1 images. The research result indicated that ecological quality of protection zone showed an overall growth trend with the help of artificial ecological restoration, and it is possible to continuously implement ecological recovery towards the protection zone.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.