In this paper, we constructed a one-dimensional convolutional neural network as a classifier model for spatial object classification. Considering that there are few available training samples obtained from actual measurement, combining with the characteristics of actual measurement data, we simulated a large amount of data for training and testing. The simulation results show that our method has a high classification accuracy and can overcome the problems existing in actual measurement, such as tracking mixed batches to a certain extent, and it can also effectively solve the problem that it is difficult to directly train neural networks because of the small number of spatial target samples, which take advantage of neural network autonomous learning and memory to reliably identify features.
In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.
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