Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

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A Unified Deep Learning Framework of Multi-scale Detectors for Geo-spatial Object Detection in High-Resolution Satellite Images

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-06288-x ◽

2021 ◽

Author(s):

Sultan Daud Khan ◽

Louai Alarabi ◽

Saleh Basalamah

Keyword(s):

Deep Learning ◽

High Resolution ◽

Object Detection ◽

Satellite Images ◽

Spatial Object ◽

Learning Framework ◽

Multi Scale ◽

High Resolution Satellite Images

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Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11030272 ◽

2019 ◽

Vol 11 (3) ◽

pp. 272 ◽

Cited By ~ 4

Author(s):

Nan Mo ◽

Li Yan ◽

Ruixi Zhu ◽

Hong Xie

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Object Detection ◽

Convolutional Neural Network ◽

Large Scale ◽

Shape Change ◽

Context Information ◽

False Detection ◽

Multi Scale

In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change.

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