A Method of Segmentation for Hyper spectral & Medical Images Based on Color Image Segmentation

The paper propose an original and simple segmentation strategy based on the EM approach for hyper spectral images . In a first step, to simplify the input color textured image into a color image without texture. The final segmentation is simply achieved by a spatially color segmentation using feature vector with the set of color values contained around the pixel to be classified. The spatial constraint allows taking into account the inherent spatial relationships of any image and its colours. This approach provides effective PSNR for the segmented image. These results omit the better performance athe segmented images are compared with Watershed & Region Growing Algorithm. This approach provides the effective segmentation for the Spectral Images & Medical Images. With proposed approach it can be fascinated that the data obtained from the segmentation can provide accurate information from the huge image

Single Tree Detection from Airborne Laser Scanning Data: A Stochastic Approach

Characterizing and monitoring forests are of great scientific and managerial interests, such as understanding the global carbon circle, biodiversity conservation and management of natural resources. As an alternative or compliment to traditional remote sensing techniques, airborne laser scanning (ALS) has been placed in a very advantageous position in forest studies, for its unique ability to directly measure the distribution of vegetation materials in the vertical direction, as well as the terrain beneath the forest canopy. Serving as basis for tree-wise forest biophysical parameter and species information retrieval, single tree detection is a very motivating research topic in forest inventory. The objective of the study is to develop a method from the perspective of computer vision to detect single trees automatically from ALS data. For this purpose, this study explored different aspects of the problem. It starts from an improved pipeline for canopy height model (CHM) generation, which alleviates the distortion of tree crown shapes presented on CHMs resulted from conventional procedures due to the shadow effects of ALS data and produces pit-free CHM. The single tree detection method consists of a hybrid framework which integrates low-level image processing techniques, i.e. local maxima filtering (LM) and marker-controlled watershed segmentation (MCWS), into a high-level probabilistic model. In the proposed approach, tree crowns in the forest plot are modelled as a configuration of circular objects. The configuration containing the best possible set of detected tree objects is estimated by a global optimization solver in a probabilistic framework. The model features an accelerated optimization process compared with classical stochastic models, e.g. marked point processes. The parameter estimation is another issue: the study investigated both a reference-based supervised and an Expectation-Maximization (EM) based unsupervised method to estimate the parameters in the model. The model was tested in a temperate mature coniferous forest in Ontario, Canada, as well as simulated coniferous forest plots with various degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering based methods, thus increasing the overall detection accuracy by approximately 10% on all of the datasets

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

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

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl