Multi-image classification and compression using vector quantization

Vector Quantization (VQ) is an image processing technique based on statistical clustering, and designed originally for image compression. In this dissertation, several methods for multi-image classification and compression based on a VQ design are presented. It is demonstrated that VQ can perform joint multi-image classification and compression by associating a class identifier with each multi-spectral signature codevector. We extend the Weighted Bayes Risk VQ (WBRVQ) method, previously used for single-component images, that explicitly incorporates a Bayes risk component into the distortion measure used in the VQ quantizer design and thereby permits a flexible trade-off between classification and compression priorities. In the specific case of multi-spectral images, we investigate the application of the Multi-scale Retinex algorithm as a preprocessing stage, before classification and compression, that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The goals of this research are four-fold: (1) to study the interrelationship between statistical clustering, classification and compression in a multi-image VQ context; (2) to study mixed-pixel classification and combined classification and compression for simulated and actual, multispectral and hyperspectral multi-images; (3) to study the effects of multi-image enhancement on class spectral signatures; and (4) to study the preservation of scientific data integrity as a function of compression. In this research, a key issue is not just the subjective quality of the resulting images after classification and compression but also the effect of multi-image dimensionality on the complexity of the optimal coder design

The impact of spatial resolution on riparian leaf area index modelling using remote sensing

This thesis investigated the impact of differing sensor spatial resolutions on leaf area index (LAI) modelling. Airborne images along with ground measurements of LAI were acquired for riparian areas along the Oldman River in southern Alberta. Airborne images were spatially resampled to spatial resolutions between 18 cm and 500 m, and the Modified Simple Ratio (MSR) was calculated from the imagery. LAI regression models were created at each spatial resolution, and changes in the relationship between MSR and LAI were observed at each spatial resolution, as well as changes in the modelled LAI estimates. The relationship between MSR and LAI was scale invariant at spatial resolutions as low as 10 m, and only moderately changed until 30 m. MSR and predicted LAI gradually reduced as resolution coarsened further, with large changes occurred beyond 100 m. No relationship was evident between MSR and LAI at spatial resolutions coarser than 300 m

Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring

Ph.DDOCTOR OF PHILOSOPH

Estimating leaf area index and aboveground biomass by empirical modeling using SPOT HRVIR satellite imagery in the Taita Hills, SE Kenya

The indigenous forests of the Taita Hills, SE Kenya, boast a vast biodiversity and provide several vital ecosystem services to local communities. Population growth and land use change pressures have resulted in a significant decrease in indigenous forest cover in the Taita Hills in recent decades. Quantifying the aboveground biomass (agb) and carbon sequestration capacity of the Taita forests provides a strong argument for striving for their more efficient protection in the context of UN-REDD programme. Although the role of tropical forests as global carbon sinks has been widely recognized, their agb and leaf area index (LAI) remain uncertain. Optical remote sensing (RS) provides a cost-effective means of LAI and agb estimation in remote areas, but empirical modeling using remote sensor data has limited certainty in densely vegetated tropical forests. The agb and LAI of the Taita Hills were estimated using empirical regression modeling by relating in situ data (n = 181 for agb, n = 172 for LAI) and spectral vegetation indices (SVIs) derived from SPOT HRVIR optical remote sensing data. Field plots (20 m x 20 m = 0.04 ha) were located in indigenous (n = 80) and exotic (n = 83) forests, woodlands (n = 9) and agroforestry areas (n = 9). In situ LAI was derived from hemispherical photography (HP) using Lang's approach and the foliage clumping correction method by Chen & Cihlar. In situ agb was estimated using allometric equations which relate agb with tree parameters such as tree diameter at breast height. Empirical relations between the response variables (agb, LAI) and SVIs were utilized in predictive regression modeling. The predictor variables were selected using forward stepwise regression based on Akaike Information Criterion (AIC) values. The regression models resulted having only one predictor each due to the redundancy of the SVIs. Also topography-based predictor variables were tested, but they were poorly or not at all related with LAI and agb. The models performed moderately (D2 = 0.62 for LAI model, D2 = 0.41 for agb model). The total agb and carbon sequestration of the study area were estimated as 4.264 Tg and 2.132 Tg C, respectively. Mean agb densities of the indigenous forests and the whole study area were estimated as 463 ± 190 Mg ha-1 and 126 ± 115 Mg ha-1, respectively. Mean in situ LAI of the indigenous forests and all plots were estimated as 3.66 ± 0.44 and 3.12 ± 0.84, respectively. Indigenous plots had the highest mean in situ agb density and LAI values compared to exotic forests, woodlands and agroforestry areas (ANOVA p < 0.001). The RMSE values of the models were 0.59 (18.6 %) for LAI and 376.85 Mg ha-1 (82.9 %) for agb. The agb model was negatively biased (bias: -107.1 Mg ha-1, 23.6 %), but there was no statistically significant bias in the LAI model. The resulting agb estimates are rather high due to high in situ agb values, partly resulting from the emphasized contribution of very large trees to biomass on small plots. LAI values are quite low for dense tropical forests due to indirect estimation method using HP, but still comparable with other similar studies. As expected, the modeling performance was impaired by SVI saturation effect in relation to LAI and agb. The agb model was biased most likely due to the use of transformed variables in linear regression. The predictive models are not transferable to other regions as such, for the relative prediction performance of SVIs is case-specific and the model parameters have to be estimated using in situ data for each site. In order to improve the model credibility, a more extensive dataset based on a random or a systematic sample should be used, having larger plot size and containing more observations with low LAI and agb values.Kaakkois-Keniassa sijaitsevien Taitavuorten alkuperäismetsissä on erittäin monimuotoinen luonto, ja ne tarjoavat useita tärkeitä ekosysteemipalveluita paikallisyhteisöille. Väestönkasvusta ja maankäytön muutospaineista johtuen alkuperäismetsien pinta-ala on pienentynyt viime vuosikymmenien aikana huomattavasti. Taitavuorten metsien maanpäällisen biomassan ja hiilensidontakapasiteetin määrällinen arviointi YK:n UN-REDD ohjelman viitekehyksessä toimii vahvana perusteena alkuperäismetsien tehokkaamman suojelun puolesta ponnistelemiseksi. Trooppiset metsät on tunnistettu tärkeiksi hiilinieluiksi, mutta niiden maanpäällinen biomassa ja lehtialaindeksi (LAI) tunnetaan huonosti. Optinen kaukokartoitus mahdollistaa kustannustehokkaan LAIn ja biomassan arvioinnin syrjäisillä seuduilla, mutta trooppisten metsien empiirinen mallinnus kaukokartoitusaineiston pohjalta on osin epävarmaa. Taitavuorten maanpäällinen biomassa ja LAI arvioitiin mallintamalla empiirisesti maastoaineiston ja SPOT HRVIR -kaukokartoitusaineistosta johdettujen kasvillisuusindeksien välillä havaittuja suhteita. 0.04 hehtaarin (20 m x 20 m) koealoja oli yhteensä 181 kpl, joista 80 alkuperäismetsissä, 83 istutusmetsissä, 9 muilla metsämailla ja 9 peltometsäviljelmillä. Maastossa mitattu LAI johdettiin hemisfäärivalokuvista Langin metodia käyttäen. Lehvästön ei-satunnaisen tilajakauman vaikutus huomioitiin Chenin ja Cihlarin korjausta käyttäen. Maastoaineiston biomassa arvioitiin käyttäen allometrisia biomassayhtälöitä, joissa biomassa arvioidaan puustoparametrien, kuten puun halkaisijan, pohjalta. Ennustemallien selittäjät valittiin eteenpäin askeltavalla regressiolla käyttäen Akaiken informaatiokriteeriä (AIC). Kasvillisuusindeksien informaatiosisällön toisteisuudesta johtuen regressiomalleihin valikoitui vain yhdet selittäjät. Myös topografiaan perustuvia muuttujia testattiin, mutta niiden ja vasteiden välillä havaittiin heikko tai ei lainkaan riippuvuutta. Mallien selitysaste oli kohtuullinen (LAI-malli: D2 = 0.62, biomassamalli: D2 = 0.41). Tutkimusalueen maanpäällinen kokonaisbiomassa oli 4.264 Tg ja kokonaishiilensidonta 2.132 Tg. Keskimääräinen biomassatiheys oli alkuperäismetsissä 463 ± 190 Mg ha-1 ja koko tutkimusalueella 126 ± 115 Mg ha-1. Keskimääräinen maastossa mitattu LAI oli alkuperäismetsissä 3.66 ± 0.44 ja kaikilla koealoilla 3.12 ± 0.84. Alkuperäismetsien maastossa mitatut keskimääräiset biomassa- ja LAI-arvot olivat tilastollisesti merkitsevästi korkeampia kuin muilla koealoilla (yksisuuntainen varianssianalyysi, p < 0.001). LAI-mallin RMSE-arvo oli 0.59 (18.6 %) ja biomassamallin RMSE-arvo 376.85 Mg ha-1 (82.9%). Biomassamalli oli negatiivisesti harhainen (harha: -107.1 Mg ha-1, 23.6 %), mutta LAI-mallissa ei havaittu tilastollisesti merkitsevää harhaa. Mallinnetut biomassa-arvot ovat korkeita johtuen maastoaineiston korkeista biomassalukemista. Pienille koealoille osuvat hyvin suuret puut vaikuttavat arvioihin huomattavasti. Mallinnetut LAI-arvot ovat vertailukelpoisia muiden vastaavien tutkimusten kanssa, mutta silti matalahkoja tiheäkasvuisille trooppisille metsille. Tämä johtuu käytetystä epäsuorasta optisesta LAIn arviointimenetelmästä, hemisfäärivalokuvauksesta. Kasvillisuusindeksien kyllästyminen suhteessa vastemuuttujiin heikensi odotetusti mallien ennustekykyä. Biomassamallin harhaisuus johtui todennäköisesti muunnettujen muuttujien käytöstä lineaarisessa regressiossa. Mallit eivät ole yleistettävissä muille alueille sellaisinaan, sillä kasvillisuusindeksien keskinäinen paremmuus on tapauskohtaista ja mallien parametrien määrittäminen vaatii maastoaineistoa tutkimusalueelta. Luotettavampien mallien rakentamiseksi tarvittaisiin systemaattiseen tai satunnaisotantaan perustuva laajempi aineisto, joka sisältää enemmän matalan LAIn ja biomassan havaintoja ja jossa koealat ovat suurempia

Remote sensing of endangered tree species in the fragmented Dukuduku Indigenous Forest of KwaZulu-Natal, South Africa.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Pietermaritzburg, 2016.Abstract available in PDF file

The 26th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

This document is a compilation of technical papers presented at the 26th Annual PTTI Applications and Planning Meeting. Papers are in the following categories: (1) Recent developments in rubidium, cesium, and hydrogen-based frequency standards, and in cryogenic and trapped-ion technology; (2) International and transnational applications of Precise Time and Time Interval technology with emphasis on satellite laser tracking, GLONASS timing, intercomparison of national time scales and international telecommunications; (3) Applications of Precise Time and Time Interval technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; (4) Applications of PTTI technology to evolving military communications and navigation systems; and (5) Dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communications satellites