Chengfan Li1,2, Lan Liu3,*, Junjuan Zhao1, Xuefeng Liu4
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202
Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >
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