Customizing kernel functions for SVM-based hyperspectral image classification

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating ";relevance"; between band information and ground trut

Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods

Hyperspectral images show similar statistical properties to natural grayscale or color photographic images. However, the classification of hyperspectral images is more challenging because of the very high dimensionality of the pixels and the small number of labeled examples typically available for learning. These peculiarities lead to particular signal processing problems, mainly characterized by indetermination and complex manifolds. The framework of statistical learning has gained popularity in the last decade. New methods have been presented to account for the spatial homogeneity of images, to include user's interaction via active learning, to take advantage of the manifold structure with semisupervised learning, to extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes. This tutuorial reviews the main advances for hyperspectral remote sensing image classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201

Computer Vision Algorithms For An Automated Harvester

Image classification and segmentation are the two main important parts in the 3D vision system of a harvesting robot. Regarding the first part, the vision system aids in the real time identification of contaminated areas of the farm based on the damage identified using the robot’s camera. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve the recognition accuracy and efficiency of the robot. Initially, a median filter is applied to remove the inherent noise in the colored image. SIFT features of the image are then extracted and computed forming a vector, which is then quantized into visual words. Finally, the histogram of the frequency of each element in the visual vocabulary is created and fed into an SVM classifier, which categorizes the mushrooms as either class one or class two. Our preliminary results for image classification were promising and the experiments carried out on the data set highlight fast computation time and a high rate of accuracy, reaching over 90% using this method, which can be employed in real life scenario. As pertains to image Segmentation on the other hand, the vision system aids in real time identification of mushrooms but a stiff challenge is encountered in robot vision as the irregularly spaced mushrooms of uneven sizes often occlude each other due to the nature of mushroom growth in the growing environment. We address the issue of mushroom segmentation by following a multi-step process; the images are first segmented in HSV color space to locate the area of interest and then both the image gradient information from the area of interest and Hough transform methods are used to locate the center position and perimeter of each individual mushroom in XY plane. Afterwards, the depth map information given by Microsoft Kinect is employed to estimate the Z- depth of each individual mushroom, which is then being used to measure the distance between the robot end effector and center coordinate of each individual mushroom. We tested this algorithm under various environmental conditions and our segmentation results indicate this method provides sufficient computational speed and accuracy

Spectral Textile Detection in the VNIR/SWIR Band

Dismount detection, the detection of persons on the ground and outside of a vehicle, has applications in search and rescue, security, and surveillance. Spatial dismount detection methods lose e effectiveness at long ranges, and spectral dismount detection currently relies on detecting skin pixels. In scenarios where skin is not exposed, spectral textile detection is a more effective means of detecting dismounts. This thesis demonstrates the effectiveness of spectral textile detectors on both real and simulated hyperspectral remotely sensed data. Feature selection methods determine sets of wavebands relevant to spectral textile detection. Classifiers are trained on hyperspectral contact data with the selected wavebands, and classifier parameters are optimized to improve performance on a training set. Classifiers with optimized parameters are used to classify contact data with artificially added noise and remotely-sensed hyperspectral data. The performance of optimized classifiers on hyperspectral data is measured with Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The best performances on the contact data are 0.892 and 0.872 for Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs), respectively. The best performances on the remotely-sensed data are AUC = 0.947 and AUC = 0.970 for MLPs and SVMs, respectively. The difference in classifier performance between the contact and remotely-sensed data is due to the greater variety of textiles represented in the contact data. Spectral textile detection is more reliable in scenarios with a small variety of textiles