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2019, Volume 2, Issue 4: 299-314. Doi: 10.3934/mfc.2019019

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Big Map R-CNN for object detection in large-scale remote sensing images

a.
FIST LAB, School of Information Science and Engineering, Yunnan University Kunming, 650091, Yunnan, China
b.
Yunnan Union Vision Technology Co Ltd. Kunming, 650091, Yunnan, China
c.
School of Software, Yunnan University Kunming, Yunnan University Kunming, 650091, Yunnan, China

^* Corresponding author: Dapeng Tao
^* Corresponding author: Dapeng Tao

Published: December 2019

Abstract / Introduction Full Text(HTML) Figure(6) / Table(8) Related Papers Cited by

Abstract

Detecting sparse and multi-sized objects in very high resolution (VHR) remote sensing images remains a significant challenge in satellite imagery applications and analytics. Difficulties include broad geographical scene distributions and high pixel counts in each image: a large-scale satellite image contains tens to hundreds of millions of pixels and dozens of complex backgrounds. Furthermore, the scale of the same category object can vary widely (e.g., ships can measure from several to thousands of pixels). To address these issues, here we propose the Big Map R-CNN method to improve object detection in VHR satellite imagery. Big Map R-CNN introduces mean shift clustering for quadric detecting based on the existing Mask R-CNN architecture. Big Map R-CNN considers four main aspects: 1) big map cropping to generate small size sub-images; 2) detecting these sub-images using the typical Mask R-CNN network; 3) screening out fragmented low-confidence targets and collecting uncertain image regions by clustering; 4) quadric detecting to generate prediction boxes. We also introduce a new large-scale and VHR remote sensing imagery dataset containing two categories (RSI LS-VHR-2) for detection performance verification. Comprehensive evaluations on RSI LS-VHR-2 dataset demonstrate the effectiveness of the proposed Big Map R-CNN algorithm for object detection in large-scale remote sensing images.

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Mathematics Subject Classification: Primary: 68T10, 68T45.

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Figure 1. Motivation for the proposed method. (a) Remote sensing scene of Madrid Airport. (b) Remote sensing scene of the South China Sea. These examples are from the RSI LS-VHR-2 dataset. The targets in the images are indicated by red cicles. The remote sensing scenes show the characteristics of large scale, high resolution, and relatively sparse target distribution, which means that existing methods are suboptimal for detection

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Figure 2. The scheme of Big Map R-CNN, containing three main components: 1) cropping the input big map in the form of a sliding window; 2) detecting each sub-image sequentially and filtering possible object areas; 3) using mean shift clustering to precisely locate candidate object areas, cropping the new sub-images containing possible objects, and using quadric-detecting to judge whether there is an object or not

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Figure 3. Large-scale image cropping

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Figure 5. PRCs of the proposed Big Map R-CNN method and three other state-of-the-art detection methods (YOLOv3, Faster R-CNN, and Mask R-CNN). (a) is the PRC of the four methods for aircraft when IoU = 0.5; (b) is the PRC of the four methods for aircraft when IoU = 0.75; (c) is the PRC of the four methods for ships when IoU = 0.5; (d) is the PRC of the four methods for ships when IoU = 0.75

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Figure 4. Some examples from the RSI LS-VHR-2 dataset

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Figure 6. Detection comparisons of the different methods. (a) Typical Mask R-CNN for aircraft; (b) Big Map R-CNN for aircraft; (c) typical Mask R-CNN for ships; (d) Big Map R-CNN for ships. The true positives are indicated by green rectangles, the false negatives are indicated by red circles, and the bounding boxes that deviate from the ground truth are indicated by red rectangles

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Table Ⅰ. DESCRIPTION OF THE RSI LS-VHR-2 DATASET

Label	Name	Total instances	Complete instances	Fragmentary instances	Scene class	Images	Image width	Sub-images
1	aircraft	103917	85975	17942	203	2858	6000-15000	62129
2	ship	68436	54386	14050	30	397	5000-18000	53860

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Table Ⅱ. DETAILS OF THE TEST IMAGES

Label	Scale(pixels)	Images	Instances	Sub-images
aircraft	$ 8000\times8000 $	5	272	980
ship	$ 8000\times8000 $	5	225	980

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Table Ⅵ. PARAMETER SETTING OF Mask R-CNN AND Big Map R-CNN

Input Size	Per Batch Size	Max Iteration	Anchor Stride	Base Learning Rate	Steps	Weight Decay	NMS Threshold	Momentum
600	8	90000	(4, 8, 16, 32, 64)	0.01	(60000, 80000)	0.0001	0.7	0.9

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Table Ⅲ. PERFARMANCE COMPARISONS OF THREE DIFFERENT CROPPING SIZE IN Faster R-CNN NETWORK

Cropping Size	AP	Cost time(s)
C300	0.430	45.82
C600	0.651	13.20
C800	0.647	8.79

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Table Ⅳ. PERFORMANCE COMPARISONS OF THE FOUR METHODS ON AIRCRAFT

Method	IoU=0.5						IoU=0.75
Method	TP	FP	FN	Recall	Precision	AP	TP	FP	FN	Recall	Precision	AP
YOLOv3	213	25	59	0.783	0.895	0.727	166	72	106	0.610	0.6974	0.494
Faster R-CNN	242	55	30	0.890	0.815	0.830	189	108	83	0.695	0.636	0.618
Mask R-CNN	245	38	27	0.901	0.866	0.843	184	99	88	0.676	0.650	0.570
Big Map R-CNN	261	4	11	0.960	0.985	0.959	241	24	31	0.886	0.909	0.850

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Table Ⅴ. PERFORMANCE COMPARISONS OF THE FOUR METHODS ON SHIP

Method	IoU=0.5						IoU=0.75
Method	TP	FP	FN	Recall	Precision	AP	TP	FP	FN	Recall	Precision	AP
YOLOv3	128	53	97	0.569	0.707	0.513	66	115	159	0.293	0.365	0.213
Faster R-CNN	164	185	61	0.729	0.470	0.651	78	271	147	0.347	0.223	0.259
Mask R-CNN	166	121	59	0.738	0.578	0.661	78	209	147	0.347	0.272	0.273
Big Map R-CNN	191	49	34	0.849	0.796	0.826	133	107	92	0.591	0.554	0.546

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Table Ⅶ. THE AVERAGE PRECISION OF Mask R-CNN AND Big Map R-CNN IN RSI LS-VHR-2 DATASET

Method	Backbone	AP($ \% $)
Mask R-CNN	ResNet50	75.2
Big Map R-CNN	ResNet50	89.2

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Table Ⅷ. COMPREHENSIVE PERFORMANCE COMPARISONS OF FOUR METHODS

Method	mAP (IoU=0.5)	mAP (IoU=0.75)	Inference time(s/im)
YOLOv3	0.620	0.354	3.310
Faster R-CNN	0.741	0.439	13.254
Mask R-CNN	0.752	0.422	13.310
Big Map R-CNN	0.892	0.700	16.005

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