Javier Calpe

Semisupervised Image Classification With Laplacian Support Vector Machines

This letter presents a semisupervised method based on kernel machines and graph theory for remote sensing image classification. The support vector machine (SVM) is regularized with the unnormalized graph Laplacian, thus leading to the Laplacian SVM (LapSVM). The method is tested in the challenging problems of urban monitoring and cloud screening, in which an adequate exploitation of the wealth of unlabeled samples is critical. Results obtained using different sensors, and with low number of training samples, demonstrate the potential of the proposed LapSVM for remote sensing image classification.

Improved Fraunhofer Line Discrimination Method for Vegetation Fluorescence Quantification

This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the O2 A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.

Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images

Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, which is a typical example of a push-broom instrument exhibiting a relatively high noise component. The proposed correction method is based on the hypothesis that the vertical disturbance presents higher spatial frequencies than the surface radiance. A technique to exclude the contribution of the spatial high frequencies of the surface from the destriping process is introduced. First, the performance of the proposed algorithm is tested on a set of realistic synthetic images with added modeled noise in order to quantify the noise reduction and the noise estimation accuracy. Then, algorithm robustness is tested on more than 350 real CHRIS images from different sites, several acquisition modes (different spatial and spectral resolutions), and covering the full range of possible sensor temperatures. The proposed algorithm is benchmarked against the CHRIS reference algorithm. Results show excellent rejection of the noise pattern with respect to the original CHRIS images, especially improving the removal in those scenes with a natural high contrast. However, some low-frequency components still remain. In addition, the developed correction model captures and corrects the dependency of the noise patterns on sensor temperature, which confirms the robustness of the presented approach.

Evaluation of remote sensing of vegetation fluorescence by the analysis of diurnal cycles

Chlorophyll fluorescence (ChF) emission is a direct indicator of the photosynthetic activity of vegetation, which is a key parameter of the carbon cycle. This paper analyses chlorophyll fluorescence evolution at leaf level during a complete diurnal cycle in simulated and natural conditions, for two species under different stress conditions. Absolute spectral radiance of the ChF emission is obtained allowing a quantitative derivation of the fluorescence yield of the ChF, which correlates well with established fluorescence instruments. The studied cases show that the ChF emission is mainly driven by the photosynthetic active radiation during the whole cycle, but the fluorescence yield is severely reduced during the central hours of the day when the plant is under stress due to light and heat. Results show that the Fraunhofer Line Discriminator method is an accurate way of retrieving quantitative values of ChF from remote sensing sensors at 760 nm and suggest that the mid‐morning period is the best time of the day to maximize signal levels while identifying vegetation stress state.

Feature selection of hyperspectral data through local correlation and SFFS for crop classification

In this paper, we propose a procedure to reduce dimensionality of hyperspectral data while preserving relevant information for posterior crop cover classification. One of the main problems with hyperspectral image processing is the huge amount of data involved. In addition, pattern recognition methods are sensitive to problems associated to high dimensionality feature spaces (referred to as Hughes phenomenon of curse of dimensionality). We propose a dimensionality reduction strategy that eliminates redundant information by means of local correlation criterion between contiguous spectral bands; and a subsequent selection of the most discriminative features based on a Sequential Float Feature Selection algorithm. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer during the DAISEX99 campaign. In the experiments, we analyze the dependence on the dimension and employed metrics. The results obtained using the Gaussian Maximum Likelihood improve the classification accuracy and confirm the validity of the proposed approach. Finally, we analyze the selected bands of the input space on order to gain knowledge on the problem and to give a physical interpretation of the results.

CART-based feature selection of hyperspectral images for crop cover classification

In this paper, we propose a procedure to reduce data dimensionality while preserving relevant information for posterior crop cover classification. The huge amount of data involved in hyperspectral image processing is one of the main problems in order to apply pattern recognition techniques. We propose a dimensionality reduction strategy that eliminates redundant information and a subsequent selection of the most discriminative features based on classification and regression trees (CART). CART allow feature selection based on the classification success, it is a non-linear method and specially allows knowledge discovery. The main advantage of our proposal relies on model interpretability, since we can get qualitative information by analyzing the surrogate and main splits of the tree. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer. Even though CART do not provide the best results in classification it is useful for a previous pre-processing step of feature selection. Finally, we analyze the selected bands of the input space in order to gain knowledge on the problem and to give a physical interpretation of results.

ETAT: Expository Text Analysis Tool

Qualitative methods that analyze the coherence of expository texts not only are time consuming, but also present challenges in collecting data on coding reliability. We describe software that analyzes expository texts more rapidly and produces a notable level of objectivity ETAT (Expository Text Analysis Tool) analyzes the coherence of expository texts. ETAT adopts a symbolic representational system, known asconceptual graph structures. ETAT follows three steps: segmentation of a text into nodes, classification of the unidentified nodes, and linking the nodes with relational arcs. ETAT automatically constructs a graph in the form of nodes and their interrelationships, along with various attendant statistics and information about noninterrelated, isolated nodes. ETAT was developed in Java, so it is compatible with virtually all computer systems.

SmartSpectra: Applying multispectral imaging to industrial environments

SmartSpectra is a smart multispectral system for industrial, environmental, and commercial applications where the use of spectral information beyond the visible range is needed. The SmartSpectra system provides six spectral bands in the range 400-1000nm. The bands are configurable in terms of central wavelength and bandwidth by using electronic tunable filters. SmartSpectra consists of a multispectral sensor and the software that controls the system and simplifies the acquisition process. A first prototype called Autonomous Tunable Filter System is already available. This paper describes the SmartSpectra system, demonstrates its performance in the estimation of chlorophyll in plant leaves, and discusses its implications in real-time applications.

A novel approach to introducing adaptive filters based on the LMS algorithm and its variants

This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear time-invariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a complete arsenal of mathematical tools, already known by the students, is available to analyze the performance of the filters and obtain the key parameters to adaptive filters, e.g., speed of convergence and stability. Several variants of the basic LMS algorithm are described the same way.

Robust low-cost vision system for fruit grading

A complete modular system for fruit sorting and grading is described. Special attention is paid to the vision sub-system for measuring the degree of ripeness according to parameters programmed by the user. The main features of the system are excellent cost vs performance ratio, high scalability, robustness and open architecture. Every module can sort up to five fruits per second in two lanes simultaneously. The system has been working for half a year in several fruit packaging facilities in Spain.