Flying Target Detection and Recognition by Feature Fusion

This paper presents a near-realtime visual detection and recognition approach for flying target detection and recognition. Detection is based on fast and robust background modeling and shape extraction, while recognition of target classes is based on shape and texture fused querying on a-priori built real datasets. Main application areas are passive defense and surveillance scenarios

Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets.

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach

CHITNet: A Complementary to Harmonious Information Transfer Network for Infrared and Visible Image Fusion

Current infrared and visible image fusion (IVIF) methods go to great lengths to excavate complementary features and design complex fusion strategies, which is extremely challenging. To this end, we rethink the IVIF outside the box, proposing a complementary to harmonious information transfer network (CHITNet). It reasonably transfers complementary information into harmonious one, which integrates both the shared and complementary features from two modalities. Specifically, to skillfully sidestep aggregating complementary information in IVIF, we design a mutual information transfer (MIT) module to mutually represent features from two modalities, roughly transferring complementary information into harmonious one. Then, a harmonious information acquisition supervised by source image (HIASSI) module is devised to further ensure the complementary to harmonious information transfer after MIT. Meanwhile, we also propose a structure information preservation (SIP) module to guarantee that the edge structure information of the source images can be transferred to the fusion results. Moreover, a mutual promotion training paradigm (MPTP) with interaction loss is adopted to facilitate better collaboration among MIT, HIASSI and SIP. In this way, the proposed method is able to generate fused images with higher qualities. Extensive experimental results demonstrate the superiority of our CHITNet over state-of-the-art algorithms in terms of visual quality and quantitative evaluations

Remote Sensing of World War II Era Unexploded Bombs Using Object-Based Image Analysis and Multi-Temporal Datasets: A Case Study of the Fort Myers Bombing and Gunnery Range

During World War II, United States Army and Navy pilots trained on several hundred bombing ranges encompassing more than 12 million acres of land, leaving behind crater-scarred landscapes across the country. Post-war estimates suggest that 10-15% of aerial bombs used failed to detonate as intended, so these areas today are contaminated by a large number of dangerous unexploded bombs (UXB) which remain under the surface. Until recently, detecting UXB has been a tedious and expensive process done in three stages: (1) identifying and mapping general areas of concentrated bomb craters using historical air photos and records; (2) intensely searching these areas at a larger scale for much smaller UXB entry holes; and (3) confirming the presence of individual UXB using magnetometry or ground-penetrating radar. This research aims to streamline the workflow for stage 1 and 2 using semi-automated object-based image analysis (OBIA) methods with multi-source high spatial-resolution imagery. Using the Fort Myers Bombing and Gunnery Range in Florida as a study area, this thesis determines what OBIA software and Imagery is best at locating UXB in this environment. I assess the use of LiDAR-derived DEMs, historical air photos and high-resolution color digital orthophotos in Feature Analyst and Imagine Objective, and discuss optimal inputs and configurations for UXB searches in karst wetlands. This methodology might be applied by the detection and clearance industry in former war zones, and aid in restoring former training ranges to safe land uses in the U.S

Identification of landmines in thermal infrared images

This paper explores the detection of landmines using thermal images acquired in military context. The conditions in which the images are obtained have a direct influence on the methods used to perform the automatic detection of landmines through image processing techniques. The proposed methodology follows two main phases: acquisition of thermal images and its processing. In the first phase, four different experiences were prepared to analyze the factors that influence the quality of the detection. In the second phase was conducted the image processing on a set of images based on classification techniques using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms. The classification was performed on a set of features extracted from ROI’s obtained by a sliding window. A second approach was also implemented based on segmentation using thresholds. The results achieved allow to identify factors that influence the detection of the mines: the burial depth, the presence of vegetation on the surface and the time of the day at which images were obtained. The optimal classification was obtained with the KNN classifier with 40 features selected with Sequential Backward Selection (SBS), and using the distance metric of correlation.info:eu-repo/semantics/publishedVersio

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition