Remote sensing image detection and segmentation technology usually needs to extract image features and mine the deep features of images through deep learning algorithm. However

traditional imaging features (e.g.

color

texture

spatial relationship) cannot fully reflect the semantic information of the images

while single/sequential algorithm cannot fully exploit the deep features and the contextual semantic information of the images. Aiming at the above challenges

in this paper

the spatial relation features are mapped into real dense vectors by word embedding

which are combined with color and texture features. Further

we propose a new parallel algorithm referred to as attention graph convolution networks and independently recurrent neural network (ATGIR) based on graph convolution network and independent recurrent neural network under attention mechanism for remote sensing image detection and segmentation. Our algorithm first assigns probabilistic weights to the combined features based on attention mechanism; then extracts deep features based on the features with high weights to generate labels with directions by using graph convolution network (GCNs) algorithms

extracts contextual semantic information of the images by using the independently recurrent neural network (IndRNN) algorithm; finally

our algorithm realizes image detection and segmentation by using Sigmoid.For remote sensing image detection and segmentation of populous euphratica forest as an instance

we prove that our feature extraction method and proposed ATGIR algorithm can effectively improve the detection and segmentation tasks.