A miniaturized flume experiment was carried out to measure impact forces of viscous debris flow. The flow depth (7.2–11.2 cm), velocity (2.4–5.2 m/s) and impact force were recorded during the experiment. The impact process of debris flow can be divided into three phases by analyzing the variation of impact signals and flow regime. The three phases are the sudden strong impact of the debris flow head, continuous dynamic pressure of the body and slight static pressure of the tail. The variation of impact process is consistent with the change in the flow regime. The head has strong–rapid impact pressure, which is shown as a turbulent‐type flow; the body approximates to steady laminar flow. Accordingly, the process of debris flows hitting structures was simplified to a triangle shape, ignoring the pressure of the tail. In order to study the distribution of the debris flow impact force at different depths and variation of the impact process over time, the impact signals of slurry and coarse particles were separated from the original signals using wavelet analysis. The slurry's dynamic pressure signal appears to be a smooth curve, and the peak pressure is 12–34 kPa when the debris flow head hits the sensors, which is about 1.54 ± 0.36 times the continuous dynamic pressure of the debris flow body. The limit of application of the empirical parameter α in the hydraulic formula was also noted. We introduced the power function relationship of α and the Froude number of debris flows, and proposed a universal model for calculating dynamic pressure. The impact pressure of large particles has the characteristic of randomness. The mean frequency of large particles impacting the sensor is 210 ± 50–287 ± 29 times per second, and it is 336 ± 114–490 ± 69 times per second for the debris flow head, which is greater than that in the debris flow body. Peak impact pressure of particles at different flow depths is 40–160 kPa, which is 3.2 ± 1.5 times the impact pressure of the slurry at the bottom of the flow, 3.1 ± 0.9 times the flow in the middle, and 3.3 ± 0.9 times the flow at the surface. The differences in impact frequency indicate that most of the large particles concentrate in the debris flow head, and the number of particles in the debris flow head increases with height. This research supports the study of solid–liquid two phase flow mechanisms, and helps engineering design and risk assessment in debris flow prone areas. Copyright © 2015 John Wiley & Sons, Ltd.

Detecting and monitoring landslides are hot topics in remote sensing community, particularly with the development of remote sensing technologies and the significant progress of computer vision. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images. We proposes a deep learning framework with constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.