Machine learning for object-based crop classification using multi-temporal Landsat-8 imagery

Thesis (MA)--Stellenbosch University, 2017.

ENGLISH ABSTRACT: Up-to-date and accurate crop maps are needed to update agricultural statistics, aid in yield forecasting, and are often used in environmental modelling. In situ methods are associated with high production costs and inefficient use of time, which hinder crop map production and reduce the usefulness of crop maps. Remote sensing offers an unbiased, cost-effective, and reliable way of mapping crops at a local, regional, and national scale. Currently, the use of multi-temporal optical imagery produces the most accurate crop maps. However, multi-temporal imagery often results in high feature dimensionality (large numbers of variables), which can negatively impact crop classification accuracy. It is therefore important to assess the benefits and limitations of using multi-temporal optical data for crop-type differentiation. This study undertakes this assessment by conducting several experiments based on multi-temporal Landsat-8 imagery in the Cape Winelands of the Western Cape, South Africa. The first experiment assessed the effect of pansharpening (image fusion), a pre-processing technique, on supervised, multi-temporal classification of crops. A suitable number of Landsat-8 images was collected based on a crop calendar of the study area. Two separate datasets, (comprising a standard resolution set of imagery and a pansharpened set of imagery) were used to create a range of image features. The images were then classified using several machine learning classifiers. Results showed that pansharpening had a significant positive influence on classification accuracy and that the support vector machine (SVM) classifier produced the most accurate results (95.9%). The second experiment utilized datasets produced in the first experiment to compare image analysis paradigms. The standard and pansharpened datasets were both segmented to produce image objects. Image object classification was then compared to the initial pixel-based classification to see which method was superior for crop differentiation with multi-temporal imagery. It was found that the object-based image analysis (OBIA) only slightly outperformed the pixel-based image analysis (PBIA), raising the question of whether the slight improvement in accuracy of the former approach is worth the effort of generating suitable image objects. In the third experiment, the capability of feature selection and feature extraction methods to mitigate high feature dimensionality were tested. Informed by the findings of the previous experiments, an OBIA approach with pansharpened imagery was used as input to feature selection and feature extraction. Results showed that feature selection did not improve the accuracy of the best performing classifier (SVM). It was concluded that feature selection is not necessary for crop differentiation when a relatively small set of features (< 200) is used. In general, multi-temporal Landsat-8 imagery shows much potential for producing accurate crop type maps. However, more research is required to evaluate the methodology in other areas and climates. Investigations into how crop type maps can be generated without collecting large numbers of training samples are also needed.

AFRIKAANSE OPSOMMING: Bygewerkte en akkurate kaarte van gewasse word benodig om landbou statistieke op te dateer, opbrengs te voorspel, en word dikwels in omgewingsmodellering gebruik. Tradisionele in situ-metodes word met hoë produksiekoste en ondoeltreffende gebruik van tyd geassosieer, wat die produksie van gewaskaarte belemmer en die nut van daarvan verlaag. Afstandswaarneming bied 'n onbevooroordeelde, koste-effektiewe en betroubare manier om gewasse op plaaslike, streeks- en nasionale skaal te karteer. Tans word die akkuraatste gewaskaarte met die gebruik van multi-temporele optiese beelde geproduseer. Multi-temporele beeldmateriaal lei egter dikwels tot hoë-eienskapsdimensionaliteit (groot getalle veranderlikes), wat die akkuraatheid van gewasklassifikasie negatief kan beïnvloed. Dit is dus belangrik om die voordele en beperkings van die gebruik van multi-temporele optiese data vir die differensiasie tussen gewastipes te assesseer. Hierdie studie pak hierdie assessering aan deur verskeie eksperimente, gebaseer op multi-temporele Landsat-8 beelde in die Kaapse Wynland van die Wes-Kaap, Suid-Afrika, uit te voer. Die eerste eksperiment beoordeel die effek van panverskerping (beeldfusie), 'n verwerkingstegniek wat vooraf uitgevoer word, op gekontroleerde, multi-temporele klassifikasie van gewasse. 'n Geskikte aantal Landsat-8 beelde is op grond van 'n gewasskalender van die studiegebied ingesamel. Twee afsonderlike datastelle (wat bestaan uit 'n stel beelde van standaard resolusie en 'n panverskerpte stel beelde) is gebruik om 'n verskeidenheid beeldkenmerke te skep. Die beelde is dan met behulp van verskeie masjienleerklassifiseerders geklassifiseer. Uitslae het getoon dat panverskerping 'n beduidende positiewe invloed op klassifikasie-akkuraatheid gehad het en dat die ondersteuningvektormasjien (OVM) die akkuraatste resultate (95.9%) opgelewer het. Die tweede eksperiment het datastelle, wat in die eerste eksperiment geproduseer is, gebruik om beeldontledingsparadigmas te vergelyk. Die standaard en panverskerpte datastelle is albei gesegmenteer om beeldobjekte te produseer. Klassifikasie van beeldobjekte is dan vergelyk met die aanvanklike pixel-gebaseerde klassifikasie om die beste metode vir die differensiasie van gewasse met multi-temporele beelde te bepaal. Daar is bevind dat die objekgebaseerde-beeldontleding (OGBO) net effens beter as die pixelgebaseerde-beeldontleding presteer. Die vraag is of dié effense verbetering in die akkuraatheid die moeite om gepaste beeldobjekte te genereer regverdig. In die derde eksperiment is kenmerkseleksie en kenmerk-ekstraksiemetodes se vermoë om hoë-kenmerk dimensionaliteit te versag, getoets. In die lig van die bevindinge van die vorige Stellenbosch University rimente is 'n OGBO-benadering met panverskerpte beelde as inset vir kenmerkseleksie en kenmerk-ekstraksie gebruik. Resultate het getoon dat kenmerkseleksie nie die akkuraatheid van die beste presterende klassifiseerder (OVM) verbeter het nie. Daar is bevind dat, wanneer 'n relatief klein stel eienskappe (< 200) gebruik word, kenmerkseleksie nie vir gewasdifferensiasie benodig word nie. Oor die algemeen toon multi-temporele Landsat-8-beelde baie potensiaal vir die vervaardiging van akkurate gewastipekaarte. Meer navorsing is egter nodig om die metodologie in ander gebiede en klimate te evalueer. Ondersoeke na hoe gewastipe-kaarte gegenereer kan word sonder om groot getalle opleidingsmonsters in te samel, is ook nodig.

Masters