A segmented particle swarm optimization convolutional neural network for land cover and land use classification of remote sensing images

ABSTRACT

The development of automatic classification methods in neural networks is an important topic in the field of land cover and land use (LULC) classification of remote sensing images. Here, we proposed a new segmented particle swarm convolutional neural network model (SPSO-CNN) by combining the subsection particle swarm algorithm with a convolutional neural network. The SPSO-CNN was applied to experiment of LULC classification of GF-1 high resolution remote sensing image. The results showed that SPSO-CNN achieved high precision, recall, F1 score and total precision in the LULC classification of remote sensing image with high spatial resolution, demonstrating the advantage and potential of applying SPSO-CNN to the LULC classification of remote sensing images.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was financially supported by the Major Science and Technology Programme for Water Pollution Control and Treatment [Grant number 2017ZX07203002-02 and 2012ZX07506-001].