Hierarchical Image Segmentation using The Watershed Algorithim with A Streaming Implementation

We have implemented a graphical user interface (GUI) based semi-automatic hierarchical segmentation scheme, which works in three stages. In the first stage, we process the original image by filtering and threshold the gradient to reduce the level of noise. In the second stage, we compute the watershed segmentation of the image using the rainfalling simulation approach. In the third stage, we apply two region merging schemes, namely implicit region merging and seeded region merging, to the result of the watershed algorithm. Both the region merging schemes are based on the watershed depth of regions and serve to reduce the over segmentation produced by the watershed algorithm. Implicit region merging automatically produces a hierarchy of regions. In seeded region merging, a selected seed region can be grown from the watershed result, producing a hierarchy. A meaningful segmentation can be simply chosen from the hierarchy produced. We have also proposed and tested a streaming algorithm based on the watershed algorithm, which computes the segmentation of an image without iterative processing of adjacent blocks. We have proved that the streaming algorithm produces the same result as the serial watershed algorithm. We have also discussed the extensibility of the streaming algorithm to efficient parallel implementations

Efficient multitemporal change detection techniques for hyperspectral images on GPU

Hyperspectral images contain hundreds of reflectance values for each pixel. Detecting regions of change in multiple hyperspectral images of the same scene taken at different times is of widespread interest for a large number of applications. For remote sensing, in particular, a very common application is land-cover analysis. The high dimensionality of the hyperspectral images makes the development of computationally efficient processing schemes critical. This thesis focuses on the development of change detection approaches at object level, based on supervised direct multidate classification, for hyperspectral datasets. The proposed approaches improve the accuracy of current state of the art algorithms and their projection onto Graphics Processing Units (GPUs) allows their execution in real-time scenarios

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information