Detection of leaf structures in close-range hyperspectral images using morphological fusion

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods

An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1

GPU computation of Attribute Profiles for Remote Sensing Image Classification

Classification of multi and hyperspectral remote sensing images is a common task. It usually requires a previous step consisting of a technique for extracting the spatial information from the image, being profiles a common approach. In particular, attribute profiles are based on the application of a morphological filter to the connected components of the image producing rel-evant spatial information at different levels of detail. The information is built based on attributes such as area or standard deviation. Their high computa-tional cost makes the attribute profiles good candidates for their execution on commodity GPUs. In this paper, the first parallel implementation of attribute profiles over multispectral images in CUDA for Nvidia GPUs is proposed. The GPU proposal is based on the construction of a max-tree that is traversed from the leaves to the root by merging the connected components of the tree obtaining a considerable reduction in execution time over the CPU execution

Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization

A new feature selection approach that is based on the integration of a genetic algorithm and particle swarm optimization is proposed. The overall accuracy of a support vector machine classifier on validation samples is used as a fitness value. The new approach is carried out on the well-known Indian Pines hyperspectral data set. Results confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy within an acceptable CPU processing time without requiring the number of desired features to be set a priori by users. Furthermore, the usefulness of the proposed method is also tested for road detection. Results confirm that the proposed method is capable of discriminating between road and background pixels and performs better than the other approaches used for comparison in terms of performance metrics.Rannís; Rannsóknarnámssjóður / The Icelandic Research Fund for Graduate Students.PostPrin

Techniques for the extraction of spatial and spectral information in the supervised classification of hyperspectral imagery for land-cover applications

The objective of this PhD thesis is the development of spatialspectral information extraction techniques for supervised classification tasks, both by means of classical models and those based on deep learning, to be used in the classification of land use or land cover (LULC) multi- and hyper-spectral images obtained by remote sensing. The main goal is the efficient application of these techniques, so that they are able to obtain satisfactory classification results with a low use of computational resources and low execution time

Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very basic shallow Convolutional Neural Network (CNN) architecture: a tailored loss function, and smooth- and label-based data augmentation. The tailored loss function enforces that neighborhood wavelengths have similar contributions to the features generated during training. A new label-based technique here proposed favors selection of pixels in smaller classes, which is beneficial in the presence of very few labeled pixels and skewed class distributions. To address the second question, we introduce a new sampling procedure to generate disjoint train and test set. Then the train set is used to obtain the CNN model, which is then applied to pixels in the test set to estimate their labels. We assess the efficacy of the simple neural network method on five publicly available hyperspectral images. On these images our method significantly outperforms considered baselines. Notably, with just 1% of labeled pixels per class, on these datasets our method achieves an accuracy that goes from 86.42% (challenging dataset) to 99.52% (easy dataset). Furthermore we show that the simple neural network method improves over other baselines in the new challenging supervised setting. Our analysis substantiates the highly beneficial effect of using the entire image (so train and test data) for constructing a model.Comment: Remote Sensing 201

A Novel Feature Selection Approach Based on FODPSO and SVM

A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle swarm optimization (FODPSO). The overall accuracy (OA) of a support vector machine (SVM) classifier on validation samples is used as fitness values in order to evaluate the informativity of different groups of bands. In order to show the capability of the proposed method, two different applications are considered. In the first application, the proposed feature selection approach is directly carried out on the input hyperspectral data. The most informative bands selected from this step are classified by the SVM. In the second application, the main shortcoming of using attribute profiles (APs) for spectral-spatial classification is addressed. In this case, a stacked vector of the input data and an AP with all widely used attributes are created. Then, the proposed feature selection approach automatically chooses the most informative features from the stacked vector. Experimental results successfully confirm that the proposed feature selection technique works better in terms of classification accuracies and CPU processing time than other studied methods without requiring the number of desired features to be set a priori by users.IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer Reviewe

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