In recent years, land cover classification technology based on multispectral remote sensing images has been applied to many fields of environmental monitoring. The existing methods generally rely on the information of spectral bands. However, the spectral information of monotemporal multispectral images cannot be generalized to describe the spectral characteristics of the ground objects at different times. In addition, the time-series samples will slightly change over time, and it is difficult to maintain classification performance continuously. To address these problems, we propose a method of land cover classification for multispectral images based on the time-spectrum association feature and multikernel boosting incremental learning. Our method is conducted in two main stages. (1) We propose the time-spectrum association features to acquire the seasonal spectral characteristics different ground objects. (2) To design the classifiers, we propose multikernel boosting method and introduce a multikernel boosting classification learning, which uses continuous new samples to update the weights of the classifier by low-computational and small-scale incremental learning. We test the proposed method on a public multispectral dataset from Landsat-5. The experimental results show that the extracted time-spectrum association features can better characterize the differences of different ground objects, and the proposed classifier can reach more accurate classification with gradually increasing samples over time.
KEYWORDS: Unmanned aerial vehicles, Synthetic aperture radar, Error analysis, Motion models, Signal to noise ratio, Systems modeling, Imaging systems, Data modeling, Signal processing, Scattering
Unmanned aerial vehicles (UAV) are a useful supplement to traditional synthetic aperture radar (SAR) platforms. In some cases, UAV-based SAR systems have to fly at low altitude. In this case, range-dependent phase errors due to platform motion affect the imaging quality. To solve the problem of motion compensation, an angle-dependent model and a second-order range-dependent model are introduced into autofocusing by previous researchers, but the first one relies too much on the geometric angle while the latter has limited fitting order for solution. We present a higher order range-dependent model, which can approximate analytical solution. Nevertheless, an increase in the fitting order makes the matrix in this model underdetermined. Based on the theoretical proof, this higher order model can be tackled by exploitation of compressive sensing (CS) theory. A CS reconstruction of higher order fitting coefficients is performed in the experiments, and corresponding performance analysis is given. Finally, the range-dependent phase error is compensated under the condition of low altitude.
To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.
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