Application of Convolutional Neural Network in the Segmentation and Classification of High-Resolution Remote Sensing Images

نویسندگان

چکیده

Numerous convolution neural networks increase accuracy of classification for remote sensing scene images at the expense models' space and time sophistication. This causes model to run slowly prevents realization a trade-off among running time. The loss deep characteristics as network gets deeper makes it impossible retrieve key aspects with sample double branching structure, which is bad classifying photos. We suggest dual branch inter feature dense fusion-based lightweight convolutional address this issue (BMDF-LCNN). In order prevent shallow data due development, can fully extricate from current layer through 3 x depthwise separable method structured 1 standard pooling layers, identity sections, fusion extracted features out preceding stage layer.

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ژورنال

عنوان ژورنال: Global journal of computer science and technology

سال: 2022

ISSN: ['0975-4172']

DOI: https://doi.org/10.34257/gjcstdvol22is2pg53