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Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification

Ahmad Saeed, Haasha Bin Atif, Usman Habib, Mohsin Bilal

TL;DR

This research proposes an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.

Abstract

Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet crucial task in aviation, especially combat identification. This research addresses this problem with a novel, scalable, and AI-driven solution. The primary hurdle in combat identification in remote sensing imagery is the accurate recognition of Novel/Unknown types of aircraft in addition to Known types. Traditional methods, human expert-driven combat identification and image classification, fall short in identifying Novel classes. Our methodology employs similarity learning to discern features of a broad spectrum of military and civilian aircraft. It discerns both Known and Novel aircraft types, leveraging metric learning for the identification and supervised few-shot learning for aircraft type classification. To counter the challenge of limited low-resolution remote sensing data, we propose an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner. Comparative analysis with earlier aircraft image classification methods shows that our approach is effective for aircraft image classification (F1-score Aircraft Type of 0.861) and pioneering for quantifying the identification of Novel types (F1-score Bipartitioning of 0.936). The proposed methodology effectively addresses inherent challenges in remote sensing data, thereby setting new standards in dataset quality. The research opens new avenues for domain experts and demonstrates unique capabilities in distinguishing various aircraft types, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.

Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification

TL;DR

This research proposes an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.

Abstract

Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet crucial task in aviation, especially combat identification. This research addresses this problem with a novel, scalable, and AI-driven solution. The primary hurdle in combat identification in remote sensing imagery is the accurate recognition of Novel/Unknown types of aircraft in addition to Known types. Traditional methods, human expert-driven combat identification and image classification, fall short in identifying Novel classes. Our methodology employs similarity learning to discern features of a broad spectrum of military and civilian aircraft. It discerns both Known and Novel aircraft types, leveraging metric learning for the identification and supervised few-shot learning for aircraft type classification. To counter the challenge of limited low-resolution remote sensing data, we propose an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner. Comparative analysis with earlier aircraft image classification methods shows that our approach is effective for aircraft image classification (F1-score Aircraft Type of 0.861) and pioneering for quantifying the identification of Novel types (F1-score Bipartitioning of 0.936). The proposed methodology effectively addresses inherent challenges in remote sensing data, thereby setting new standards in dataset quality. The research opens new avenues for domain experts and demonstrates unique capabilities in distinguishing various aircraft types, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.
Paper Structure (10 sections, 2 equations, 11 figures, 1 algorithm)

This paper contains 10 sections, 2 equations, 11 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed INNAR Methodology
  4. Embedding Architecture Training
  5. Bipartitioning for out-of distribution images
  6. Dataset Quality Management
  7. Experimental Results And Discussion
  8. Experimental Setup
  9. Results & Discussion
  10. Conclusion and Future Work

Figures (11)

  • Figure 1: Flow Diagram from left to Right showing a)Traditional CID , b) Image classification & c) Intelligent Known and Novel Aircraft Recognition (INNAR) to find Known and Novel class
  • Figure 2: MTARSI-INNAR b47:saeed_2023_10421449 Embedder Training Set Showcasing Remote Sensing Military & Civil Aircraft highlighting Remote Sensing challenges as illumination conditions,orientation, background, noise and capturing angles.
  • Figure 3: MTARSI-INNAR b47:saeed_2023_10421449 Novel Testing Set highlighting unique classes covering role and features as Fighter, ISR, Transport, Surveillance and Multi-Mission Platforms.
  • Figure 4: Miss labelling & cross contamination issues in MTARSI datasets. a) B-777, B-747 and Airbus being displayed as Boeing. b) B-777, B-747 and T-1A being misclassified with C-40. c) C-130, DC-4E and P3C being displayed as DC-4.
  • Figure 5: Proposed INNAR Methodology of Automated CID to identify Novel Class; where the input image gets transformed into feature vector which is then used to identify as a Known or Novel followed by classification. a) Embedder Architecture training b) Few shot learning for embedding space c) Bipartitioning for out-of distribution images d) Few-Shot Classification
  • ...and 6 more figures