Building collapse arising from destructive earthquakes is often the primary cause of casualties and economic loss. Building damage assessment is one of the top priorities in earthquake emergency work. Quad-polarimetric synthetic aperture radar (PolSAR) data not only has the advantages of radar imaging being neither exposed to sunlight nor blocked by clouds, but also contains the most abundant information of the four polarimetric channels. In many cases, the texture feature even outperforms other kinds of features. The texture features of buildings include not only spatial texture but also frequency texture. We proposed a parameter called the sector texture feature of the Fourier amplitude spectrum (STFFAS) to describe frequency-domain texture features based on the Fourier amplitude spectrum for building damage recognition. Our experimental results show that the recognition performance of the frequency texture feature performs satisfactorily for building damage information extraction.
Hyperspectral unmixing is a crucial step in hyperspectral image processing. Hyperspectral images in real scenes are saturated with spectral variability, and unmixing performance is limited. We propose the Spectral Variability Attention Net (SVA-Net). We have separately designed a Complementary Feature Enhancement Module (CFE) and a Spectral Variability Attention Mechanism to capture both the original material features in the image and other easily overlooked features. In addition, we design the improved mixing model based on augmented linear mixing model (ALMM) to better cope with the effects of spectral variability. Experiments on real datasets demonstrate the effectiveness of our model.
We propose a novel deep learning-based method that adapts the domains of images acquired by different remote sensing sensors. It adapts a lower resolution image to the domain of an an higher resolution targeted sensor. This is effective in the case of change detection, where differences between sensors, such as spatial resolution and radiometry, can hinder the detection performance and where model hallucination artifacts are unwanted. The proposed technique divides the input image into patches and uses a diffusion-based model to generate translated patches in the style of the target sensor. The translated patches are stitched together to form the output image, which provides global generative consistency. Our approach can handle images with different resolutions and tonalities. We show its effectiveness on a Sentinel-II + Planet Dove data set and demonstrate its high generation quality and contribution to enhance change detection performance.
This paper deals with the target detection problem in nonzero-mean compound Gaussian (CG) sea clutter with the generalized inverse Gaussian (GIG) texture. With the improvement of radar resolution, the CG distribution is adopted to model the sea clutter. Then, considering the characteristics of real sea clutter, the CG model with the GIG texture is applied. Furthermore, sea clutter signals are assumed to be nonzero-mean. A novel adaptive two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) detection algorithm is proposed. Firstly, the test statistic of the proposed detector with known GIG texture, mean vector (MV), and covariance matrix (CM) is derived. Secondly, replacing with the estimates of GIG texture, MV, and CM, the adaptive detector can be acquired. The numerical results indicate the performance of the proposed detector.
Rainfall is the primary landslide triggering factor in China, and the spatial-temporal hazard prediction of rainfall-induced landslides is of great practical significance. Currently, most countries and regions establish landslide hazard prediction systems based on rainfall data only, resulting in low spatial precision of hazard prediction results and a high false alarm rate. This paper proposes a hazard prediction model that considers landslide triggering factors, landslide predisposing environment, and the spatial regularity of historical landslides based on multi-modal earth observation data. The proposed model has significantly improved the spatial-temporal hazard prediction performance of rainfall-induced natural terrain landslides in Hong Kong.
