Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

Improving Hyperspectral Subpixel Target Detection Using Hybrid Detection Space

A Hyper-Spectral Image (HSI) has high spectral and low spatial resolution. As a result, most targets exist as subpixels, which pose challenges in target detection. Moreover, limitation of target and background samples always hinders the target detection performance. In this thesis, a hybrid method for subpixel target detection of an HSI using minimal prior knowledge is developed. The Matched Filter (MF) and Adaptive Cosine Estimator (ACE) are two popular algorithms in HSI target detection. They have different advantages in differentiating target from background. In the proposed method, the scores of MF and ACE algorithms are used to construct a hybrid detection space. First, some high abundance target spectra are randomly picked from the scene to perform initial detection to determine the target and background subsets. Then, the reference target spectrum and background covariance matrix are improved iteratively, using the hybrid detection space. As the iterations continue, the reference target spectrum gets closer and closer to the central line that connects the centers of target and background and resulting in noticeable improvement in target detection. Two synthetic datasets and two real datasets are used in the experiments. The results are evaluated based on the mean detection rate, Receiver Operating Characteristic (ROC) curve and observation of the detection results. Compared to traditional MF and ACE algorithms with Reed-Xiaoli Detector (RXD) background covariance matrix estimation, the new method shows much better performance on all four datasets. This method can be applied in environmental monitoring, mineral detection, as well as oceanography and forestry reconnaissance to search for extremely small target distribution in a large scene

Spectral misregistration correction and simulation for hyperspectral imagery

Radiometrically calibrated radiance hyperspectral images can be converted into reflectance images using atmospheric correction in order to extract useful ground information. There are some artifacts in the converted reflectance images due to spectrally misregistered sensor and atmospheric model error. These artifacts give coherent saw-tooth effects in the spectra of the reflectance imagery. These effects degrade the performance of classification and target detection algorithms and make them difficult to compare with ground target spectra. Three spectral misregistration compensation methods were developed in order to compensate for the consistent noise effects. If a ground truth spectrum exists for a test image, the ground truth spectrum can be divided by the spectrum derived from the reflectance image. This will give a coefficient indicating the difference between the ground truth spectrum and the noisy spectrum in the reflectance image. Multiplying this coefficient spectrum and the reflectance image spectrum can correct the saw-tooth effects. The other methods use the Cubic Spline smoothing technique. Cubic Spline smoothing is a fitting algorithm with a non-local smoothing method. Cubic spline smoothing can smooth out the saw-tooth noise in the spectra then the correction coefficient can be calculated as describe above. It is important to find relatively pure and unmixed pixels for the correction coefficient. Two methods for identifying relatively pure pixels were used for this research. The first is the Uniform Region method that is to identify the pixels with small standard deviation values among neighbor pixels. The second method is the Least Ratio method that is used to calculate ratios (standard deviation between smoothed and non-smoothed spectra divided by average reflectance of the spectra) and then calculate the correction coefficient using pixels having small ratios. Spectral misregistration was also simulated using MODTRAN lookup table and DIRSIG (The Digital Imaging and Remote Sensing Image Generation) synthetic image to understand and characterize the effect of spectral misregistration. The spectral misregistration compensation algorithms were tested and verified by the performance measurement of classification and target detection algorithms for test images (real and synthetic images)