Mask R-CNN-based intelligent identification of sparse woods from remote sensing images

doi:10.6046/zrzyyg.2022158

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Abstract

There are only a few low-accuracy methods available for the feature extraction of sparse woods from remote sensing images. Moreover, there is a lack of datasets for intelligent identification. This study proposed a method for intelligent information identification of sparse woods from remote sensing images. First, a dataset was created using QGIS and Python separately to provide data support for model training. Then, feature maps were generated through feature extraction, and then regions of interest (ROIs) were extracted from the feature maps. Subsequently, these ROIs were filtered through pooling operations (ROI align) to reduce the memory consumption caused by too many ROIs in the images. Experiments show that the method proposed in this study can create datasets quickly and facilitate the identification of sparse woods from remote sensing images. Moreover, the Mask R-CNN-based intelligent identification has a target detection mean average precision (MAP) of up to 0.92.

Keywords remote sensing image sparse woods deep learning Mask R-CNN QGIS

ZTFLH:

TP751

Issue Date: 07 July 2023

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	Mingguang DIAO
	Yong LIU
	Ningbo GUO
	Wenji LI
	Jikang JIANG
	Yunxiao WANG

Cite this article:

Mingguang DIAO,Yong LIU,Ningbo GUO, et al. Mask R-CNN-based intelligent identification of sparse woods from remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 97-104.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022158 OR https://www.gtzyyg.com/EN/Y2023/V35/I2/97

Fig.1 Remote sensing image sparse woods dataset generation process

Tab.1 Mapping relationship of JSON data and vector data

Tab.2 Performance comparison of different dataset generation methods

Fig.2 Framework for intelligent recognition model of remote sensing image sparse woods

Fig.3 ResNet-FPN network architecture

Fig.4 Bilinear interpolation calculation process

Fig.5 Conversion effect of remote sensing image sparse woods intelligent recognition model result

Fig.6 Framework of intelligent identification method for sparse woods from remote sensing image

Fig.7 Remote sensing data local image and vector data of sparse woods

Fig.8 Loss function graph

Fig.9 Experimental recognition results

Fig.10 Experimental verification set accurate recall curve

Fig.11 Validation set experimental results

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