NDLI: Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images

Content Provider	IEEE Xplore Digital Library
Author	Peicheng Zhou Dingwen Zhang Gong Cheng Junwei Han
Copyright Year	2015
Description	Author affiliation: Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China (Peicheng Zhou; Dingwen Zhang; Gong Cheng; Junwei Han)
Abstract	When training a classifier in a traditional weakly supervised learning scheme, negative samples are obtained by randomly sampling. However, it may bring deterioration or fluctuation for the performance of the classifier during the iterative training process. Considering a classifier is inclined to misclassify negative examples which resemble positive ones, comprising these misclassified and informative negatives should be important for enhancing the effectiveness and robustness of the classifier. In this paper, we propose to integrate Negative Bootstrapping scheme into weakly supervised learning framework to achieve effective target detection in remote sensing images. Compared with traditional weakly supervised target detection schemes, this method mainly has three advantages. Firstly, our model training framework converges more stable and faster by selecting the most discriminative training samples. Secondly, on each iteration, we utilize the negative samples which are most easily misclassified to refine target detector, obtaining better performance. Thirdly, we employ a pre-trained convolutional neural network (CNN) model named Caffe to extract high-level features from RSIs, which carry more semantic meanings and hence yield effective image representation. Comprehensive evaluations on a high resolution airplane dataset and comparisons with state-of-the-art weakly supervised target detection approaches demonstrate the effectiveness and robustness of the proposed method.
Starting Page	318
Ending Page	323
File Size	1269853
Page Count	6
File Format	PDF
e-ISBN	9781479986880
DOI	10.1109/BigMM.2015.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training High-level feature Negative Bootstrapping Supervised learning Detectors Object detection Feature extraction Weakly supervised learning (WSL) Robustness Remote sensing Target detection Remote sensing image (RSI)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Weakly Supervised Learning for Target Detection in Remote Sensing Images

Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

Object Localization in Weakly Labeled Remote Sensing Images Based on Deep Convolutional Features

Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

Incorporating the Completeness and Difficulty of Proposals Into Weakly Supervised Object Detection in Remote Sensing Images

Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images

Object-based feature extraction and semi-supervised classification for urban change detection using high-resolution remote sensing images

FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images

Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images

Similar Documents

Weakly Supervised Learning for Target Detection in Remote Sensing Images

Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning

Object Localization in Weakly Labeled Remote Sensing Images Based on Deep Convolutional Features

Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

Incorporating the Completeness and Difficulty of Proposals Into Weakly Supervised Object Detection in Remote Sensing Images

Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images

Object-based feature extraction and semi-supervised classification for urban change detection using high-resolution remote sensing images

FCC-Net: A Full-Coverage Collaborative Network for Weakly Supervised Remote Sensing Object Detection

Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images

Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images