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

A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water

Hyperspectral imaging is a widely used technology for industrial and scientific purposes, but the high cost and large size of commercial setups have made them impractical for most basic research. Here, we designed and implemented a fully open source and low-cost hyperspectral scanner based on a commercial spectrometer coupled to custom optical, mechanical and electronic components. We demonstrate our scanner's utility for natural imaging in both terrestrial and underwater environments. Our design provides sub-nm spectral resolution between 350-950 nm, including the UV part of the light spectrum which has been mostly absent from commercial solutions and previous natural imaging studies. By comparing the full light spectra from natural scenes to the spectral sensitivity of animals, we show how our system can be used to identify subtle variations in chromatic details detectable by different species. In addition, we have created an open access database for hyperspectral datasets collected from natural scenes in the UK and India. Together with comprehensive online build- and use-instructions, our setup provides an inexpensive and customisable solution to gather and share hyperspectral imaging data

Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter

Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. In this paper, we propose a novel algorithm for hyperspectral (HS) image pansharpening with two stage guided filtering in PCA (principal component analysis) domain. In the first stage, we first downsample the high resolution RGB image to the same spatial resolution of original low-resolution HS image, and use guided filter to transfer the image details (e.g. edge) of the downsampled RGB image to the original HS image in the PCA domain In the second stage, we perform upsampling on the resulting HS image from the first stage by using original high-resolution RGB image and guided filter in PCA domain. This yields a clear improvement over an older approach with one stage guided filtering in PCA domain. Experimental results on fusion of a low spatial-resolution Thermal Infrared HS image and a high spatial-resolution visible RGB image from the 2014 IEEE GRSS Data Fusion Contest, are very encouraging

Structure-Preserving Spectral Reflectance Estimation using Guided Filtering

Light spectra are a very important source of information for diverse classification problems, e.g., for discrimination of materials. To lower the cost for acquiring this information, multispectral cameras are used. Several techniques exist for estimating light spectra out of multispectral images by exploiting properties about the spectrum. Unfortunately, especially when capturing multispectral videos, the images are heavily affected by noise due to the nature of limited exposure times in videos. Therefore, models that explicitly try to lower the influence of noise on the reconstructed spectrum are highly desirable. Hence, a novel reconstruction algorithm is presented. This novel estimation method is based on the guided filtering technique which preserves basic structures, while using spatial information to reduce the influence of noise. The evaluation based on spectra of natural images reveals that this new technique yields better quantitative and subjective results in noisy scenarios than other state-of-the-art spatial reconstruction methods. Specifically, the proposed algorithm lowers the mean squared error and the spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore, it is shown that the proposed reconstruction technique works out-of-the-box and does not need any calibration or training by reconstructing spectra from a real-world multispectral camera with nine channels

A novel feature fusion approach for VHR remote sensing image classification

6openInternationalInternational coauthor/editorThis article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages. In the first fusion stage, multiple features are grouped by clustering, where redundant information between different types of features is eliminated within each group. Then, features are pairwisely fused in an intergroup fusion model based on the guided filtering method. Finally, the fused feature set is imported into a classifier to generate the classification map. In this work, the original VHR spectral bands and their attribute profiles are taken as examples as input spectral and spatial features, respectively, in order to test the performance of the proposed TsF approach. Experimental results obtained on two QuickBird datasets covering complex urban scenarios demonstrate the effectiveness of the proposed approach in terms of generation of more discriminative fusion features and enhancing classification performance. More importantly, the fused feature dimensionality is limited at a certain level; thus, the computational cost will not be significantly increased even if multiple features are considered.openLiu, S.; Zheng, Y.; Du, Q.; Samat, A.; Tong, X.; Dalponte, M.Liu, S.; Zheng, Y.; Du, Q.; Samat, A.; Tong, X.; Dalponte, M