SPIRE algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 393-396).In this thesis we address the problem of estimating changes in surface reflectance in hyperspectral image cubes, under unknown multiplicative and additive illumination noise. Rather than using the Empirical Line Method (ELM) or physics-based approaches, we assumed the presence of a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise vectors, and developed algorithms which estimate reflectance using this prior information. These algorithms were developed under the additional assumptions that the illumination effects were band limited to lower spatial frequencies and that the differences in the surface reflectance from the prior were small in area relative to the scene, and have defined edges. These new algorithms were named Surface Prior Information Reflectance Estimation (SPIRE) algorithms. Spatial SPIRE algorithms that employ spatial processing were developed for six cases defined by the presence or absence of the additive noise, and by whether or not the noise signals are spatially uniform or varying. These algorithms use high-pass spatial filtering to remove the noise effects. Spectral SPIRE algorithms that employ spectral processing were developed and use zero-padded Principal Components (PC) filtering to remove the illumination noise. Combined SPIRE algorithms that use both spatial and spectral processing were also developed. A Selective SPIRE technique that chooses between Combined and Spectral SPIRE reflectance estimates was developed; it maximizes estimation performance on both modified and unmodified pixels. The different SPIRE algorithms were tested on HYDICE airborne sensor hyperspectral data, and their reflectance estimates were compared to those from the physics-based ATmospheric REMoval (ATREM) and the Empirical Line Method atmospheric compensation algorithms. SPIRE algorithm performance was found to be nearly identical to the ELM ground-truth based results. SPIRE algorithms performed better than ATREM overall, and significantly better under high clouds and haze. Minimum-distance classification experiments demonstrated SPIRE's superior performance over both ATREM and ELM in cross-image supervised classification applications. The taxonomy of SPIRE algorithms was presented and suggestions were made concerning which SPIRE algorithm is recommended for various applications.by Herbert Erik Mattias Viggh.Ph.D

Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data

Information about artists\u27 materials used in paintings, obtained from the analysis of limited micro-samples, has assisted conservators to better define treatment plans, and provided scholars with basic information about the working methods of the artists. Recently, macro-scale imaging systems such as visible-to-near infrared (VNIR) reflectance hyperspectral imaging (HSI) are being used to provide conservators and art historians with a more comprehensive understanding of a given work of art. However, the HSI analysis process has not been streamlined and currently requires significant manual input by experts. Additionally, HSI systems are often too expensive for small to mid-level museums. This research focused on three main objectives: 1) adapt existing algorithms developed for remote sensing applications to automatically create classification and abundance maps to significantly reduce the time to analyze a given artwork, 2) create an end-to-end pigment identification convolutional neural network to produce pigment maps that may be used directly by conservation scientists without further analysis, and 3) propose and model the expected performance of a low-cost fiber optic single point multispectral system that may be added to the scanning tables already part of many museum conservation laboratories. Algorithms developed for both classification and pigment maps were tested on HSI data collected from various illuminated manuscripts. Results demonstrate the potential of both developed processes. Band selection studies indicates that pigment identification from a small number of bands produces similar results to that of the HSI data sets on a selected number of test artifacts. A system level analysis of the proposed system was conducted with a detailed radiometric model. The system trade study confirmed the viability of using either individual spectral filters or a linear variable filter set-up to collect multispectral data for pigment identification of works of art

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Analysing the vegetation of energy plants by processing UAV images

Bioenergy plants are widely used as a form of renewable energy. It is important to monitor the vegetation and accurately estimate the yield before harvest in order to maximize the profit and reduce the costs of production. The automatic tracking of plant development by traditional methods is quite difficult and labor intensive. Nowadays, the application of Unmanned Aerial Vehicles (UAV) became more and more popular in precision agriculture. Detailed, precise, three-dimensional (3D) representations of energy forestry are required as a prior condition for an accurate assessment of crop growth. Using a small UAV equipped with a multispectral camera, we collected imagery of 1051 pictures of a study area in Kompolt, Hungary, then the Pix4D software was used to create a 3D model of the forest canopy. Remotely sensed data was processed with the aid of Pix4Dmapper to create the orthophotos and the digital surface model. The calculated Normalized Difference Vegetation Index (NDVI) values were also calculated. The aim of this case study was to do the first step towards yield estimation, and segment the created orthophoto, based on tree species. This is required, since different type of trees have different characteristics, thus, their yield calculations may differ. However, the trees in the study area are versatile, there are also hybrids of the same species present. This paper presents the results of several segmentation algorithms, such as those that the widely used eCognition provides and other Matlab implementations of segmentation algorithms

A Methodology for Automatic Identification of Units with Ecological Significance in Dehesa Ecosystems

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images

Light field reconstruction from multi-view images

Kang Han studied recovering the 3D world from multi-view images. He proposed several algorithms to deal with occlusions in depth estimation and effective representations in view rendering. the proposed algorithms can be used for many innovative applications based on machine intelligence, such as autonomous driving and Metaverse

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Anomaly detection and compensation for hyperspectral imagery

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-158).Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far. A generic methodology is presented for detecting and compensating anomalies from hyperspectral imagery, taking advantage of all information available -- spectral and spatial correlation and any a priori knowledge about the anomalies. An anomaly is generally defined as an undesired spatial and spectral feature statistically different from its surrounding background. Principal component analysis (PCA) and the Iterative Order and Noise (ION) estimation algorithm provide valuable tools to characterize signals and reduce noise. Various methodologies are also addressed to cope with nonlinearities in the system without much computational burden. An anomaly compensation technique is applied to specific problems that exhibit different stochastic models for an anomaly and its performance is evaluated.(cont.) Hyperspectral anomalies dealt with in this thesis are (1) cloud impact in hyperspectral radiance fields, (2) noisy channels and (3) scan-line miscalibration. Estimation of the cloud impact using the proposed algorithm is especially successful and comparable to an alternative physics-based algorithm. Noisy channels and miscalibrated scan-lines are also fairly well compensated or removed using the proposed algorithm.by Choongyeun Cho.Ph.D