In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper. (1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information. (2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient vanishing. The experimental results show that the landslide extraction model proposed in this paper is practical. It can improve the accuracy of landslide semantic segmentation.
Aiming at the problem that the periodic multiple phases modulation (PMPM) jamming method requires transmit-receive isolation, this paper proposes an azimuth intermittent periodic multiple phases modulation (IPMPM) jamming method, which can generate false target (FT) string with random amplitude fluctuation as PMPM jamming method. On this basis, the jammer performs a single/multiple time-delay repeater to control the position of the false targets (FTs) in range/azimuth direction. Both theoretical analysis and simulation experiments demonstrate the effectiveness of the azimuth IPMPM jamming method.
Pan-sharpening is an important image preprocessing technique for remote sensing that aims to enhance spatial resolution of multispectral (MS) images under the guidance of panchromatic (PAN) image while preserving spectral properties. The existing pan-sharpening methods usually adopt the globally consistent detail-injection models, neglecting the detail differences between spectral channels, which leads to imprecise spatial details and distorted spectral properties of pan-sharpening results. We propose a sparse representation-based detail-injection model for pan-sharpening that utilizes the structure similarity and detail differences between PAN and low-resolution multispectral (LRM) images at each channel, to improve the performance of pan-sharpening. Specifically, to better express the inherent detail properties of the MS image, the overcomplete dictionary of each channel is constructed from synthesized high-resolution multispectral (HRM) images. Moreover, the most proposed methods require that the spectral responses of the PAN image and the MS image cover the same wavelength range; nevertheless, most sensors cannot match this condition. To address this problem, we propose constructing coupled low-resolution and high-resolution dictionaries from LRM and synthesized HRM images so that the structure similarities can be used for detail injection. The qualitative and quantitative experimental results on various data sets demonstrate the superiority of our proposed method over the state-of-the-art methods.
In the Bohai Sea, sea ice drifting is hardly tracked due to the highly sea motion. The long satellite repeat cycles in the polar region are not suitable to the ice drift tracking in the Bohai Sea. The unique characteristics of the Geostationary Ocean Color Imager (GOCI) allow the tracking of sea ice drift on a daily basis with the use of 1-hour time intervals images (eight images per day). The optical flow method is applied to track the sea ice drift in the Bohai Sea. Experiments have shown that the sea ice vectors from the optical flow method are agreement well with the manually selected reference data.
The Bohai Sea is located in the middle latitude region, which is an important economic development zone in China. However, sea ice drift causes significant economic losses in the winter. Sea ice drifting is difficult to track due to the long satellite repeat cycles in the polar region and the rapid changes in the Bohai Sea ice. The unique characteristics of the Geostationary Ocean Color Imager (GOCI) allow tracking of sea ice drift on a daily basis with the use of 1-h time interval images (eight images per day). This study employed the GOCI data for daily 1-h sea ice drift tracking in the Bohai Sea using a maximum cross-correlation method. Sea ice drift monitoring is accomplished by tracking the distinct characteristics of sea ice samples. The sea ice drift tracking derived from the GOCI images are validated by the in-situ data and historical data in Liaodong Bay. In addition, sea ice drift in the Bohai Sea is controlled by the surface current and wind, and the current-ice drag coefficient and wind-ice drag coefficient are 0.91 and 0.03, respectively, roughly corresponding to 2.55% of the surface wind speed.
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