Shubham Chaudhary, Parima Jain, V. Jakhetiya, Sharath Chandra Guntuku, B. Subudhi
{"title":"Localizing Features with Masking for Satellite and Debris Classification","authors":"Shubham Chaudhary, Parima Jain, V. Jakhetiya, Sharath Chandra Guntuku, B. Subudhi","doi":"10.1109/ICIPC53495.2021.9620178","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620178","url":null,"abstract":"In this work, we propose a localization and masking-based satellite and debris classification technique. SPAce-craft Recognition leveraging Knowledge of space environment (SPARK) dataset consists of 120K images where both RGB and corresponding Depth images are available. However, the depth images are noisy and inaccurate and significantly affect the classification task performance. To address this issue, we first create mask images of the RGB images which are used as input to the Convolutional Neural Network (CNN) for efficient classification of different satellites and debris. The depth images are first de-noised and hole filled using a simple morphological opening operation. Then masked images are calculated using both RGB and processed depth images. This masking operation provides two advantages: 1. it removes noise and fills the holes in the depth images and 2. it highlights satellites and debris while suppressing other information which does not contribute towards the classification task. We use the pre-trained EfficientNet B4 architecture and fine-tuned it with an edition of Global average pooling (GAP) and three dense layers. Our results show that the inclusion of the masking operation significantly improves the overall classification performance, achieving 97.76% accuracy on the validation data.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"RGB-D Based Multimodal Convolutional Neural Networks for Spacecraft Recognition","authors":"Nouar Aldahoul, H. A. Karim, Mhd Adel Momo","doi":"10.1109/ICIPC53495.2021.9620192","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620192","url":null,"abstract":"Spacecraft recognition is a significant component of space situational awareness (SSA), especially for applications such as active debris removal, on-orbit servicing, and satellite formation. The complexity of recognition in actual space imagery is caused by a large diversity in sensing conditions, including background noise, low signal-to-noise ratio, different orbital scenarios, and high contrast. This paper addresses the previous problem and proposes multimodal convolutional neural networks (CNNs) for spacecraft detection and classification. The proposed solution includes two models: 1) a pre-trained ResNet50 CNN connected to a support vector machine (SVM) classifier for classification of RGB images. 2) an end-to-end CNN for classification of depth images. The experiments conducted on a novel SPARK dataset was generated under a realistic space simulation environment and has 150k of RGB images and 150k of depth images with 11 categories. The results show high performance of the proposed solution in terms of accuracy (89 %), F1 score (87 %), and Perf metric (1.8).","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127387922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. A. Musallam, Vincent Gaudillière, Enjie Ghorbel, Kassem Al Ismaeil, M. Perez, Michel Poucet, Djamila Aouada
{"title":"Spacecraft Recognition Leveraging Knowledge of Space Environment: Simulator, Dataset, Competition Design and Analysis","authors":"M. A. Musallam, Vincent Gaudillière, Enjie Ghorbel, Kassem Al Ismaeil, M. Perez, Michel Poucet, Djamila Aouada","doi":"10.1109/ICIPC53495.2021.9620184","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620184","url":null,"abstract":"SPARK represents the first edition of the SPAcecraft Recognition leveraging Knowledge of space environment competition organized by the Interdisciplinary Centre for Security, Reliability and Trust (SnT) in conjunction with the 2021 IEEE International Conference in Image Processing (ICIP 2021). By providing a unique synthetic dataset composed of 150k annotated multi-modal images, SPARK aims at encouraging researchers to develop innovative solutions for space target recognition and detection. This paper introduces the proposed dataset and provides a global analysis of the results obtained for the 17 submissions.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123137243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spark Challenge: Multimodal Classifier for Space Target Recognition","authors":"I. Lahouli, M. Jarraya, G. Aversano","doi":"10.1109/ICIPC53495.2021.9620183","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620183","url":null,"abstract":"In this paper, we propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders (AE)s to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121977617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}