Further results on dissimilarity spaces for hyperspectral images RF-CBIR

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.Comment: In Pattern Recognition Letters (2013

Silica in a Mars analog environment: Ka'u Desert, Kilauea Volcano, Hawaii

Airborne Visible/Near-Infrared Imaging Spectrometer (AVIRIS) data acquired over the Ka'u Desert are atmospherically corrected to ground reflectance and used to identify the mineralogic components of relatively young basaltic materials, including 250–700 and 200–400 year old lava flows, 1971 and 1974 flows, ash deposits, and solfatara incrustations. To provide context, a geologic surface units map is constructed, verified with field observations, and supported by laboratory analyses. AVIRIS spectral end-members are identified in the visible (0.4 to 1.2 μm) and short wave infrared (2.0 to 2.5 μm) wavelength ranges. Nearly all the spectral variability is controlled by the presence of ferrous and ferric iron in such minerals as pyroxene, olivine, hematite, goethite, and poorly crystalline iron oxides or glass. A broad, nearly ubiquitous absorption feature centered at 2.25 μm is attributed to opaline (amorphous, hydrated) silica and is found to correlate spatially with mapped geologic surface units. Laboratory analyses show the silica to be consistently present as a deposited phase, including incrustations downwind from solfatara vents, cementing agent for ash duricrusts, and thin coatings on the youngest lava flow surfaces. A second, Ti-rich upper coating on young flows also influences spectral behavior. This study demonstrates that secondary silica is mobile in the Ka'u Desert on a variety of time scales and spatial domains. The investigation from remote, field, and laboratory perspectives also mimics exploration of Mars using orbital and landed missions, with important implications for spectral characterization of coated basalts and formation of opaline silica in arid, acidic alteration environments

Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image

A Review on Advances in Automated Plant Disease Detection

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images