Evaluation of pixel based and object based classification methods for land cover mapping with high spatial resolution satellite imagery, in the Amazonas, Brazil

In the state of Acre, Brazil, there is ongoing land use change, where inhabitants of this part of the Amazonian rainforest practice shifting agriculture. Practicing this type of agriculture is, according to the SKY Rainforest Rescue organization, damaging to forest ecosystems. This organization aims to educate people in how to maintain sustainable agriculture. By monitoring this shift in agricultural practices with the use of remotely sensed data, the organization can follow the development. In this thesis, an image with high spatial resolution from the SPOT-5 satellite is used to evaluate which classification method is most appropriate for monitoring land use change in this specific area. Three methods are tested; two pixels based and one object based. The pixel based methods are the Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel and the Maximum Likelihood Classifier (MLC), and the object based method is segmented with Multi Resolution Segmentation (MRS) and classified with the k-Nearest Neighbor (kNN). The parameters gamma and penalty parameter C in the SVM with an RBF kernel were estimated by a k-fold cross validation and grid search method; and for the MLC, an assumption that each class had an equal probability distribution was made. For the object based approach the first step was segmentation; for the MRS there are three parameters: scale, shape and compactness. The scale parameter was set by using an algorithm that was based on comparing local variance; shape and compactness were defined based on previous studies and visual evaluation of the segments. All three methods will produce two classified maps each; one where the feature space consists of the three original wavebands (green, red and NIR) and one where the feature space is of six dimensions that include the original three wavebands and three texture derivations, one from each original band. The texture is derived from the co-occurrence GLCM method, which can be used to calculate 14 different texture measures. The three most suitable texture derivations were the contrast texture measure from the green and NIR band, and an entropy texture derived from the red band. When combining these three texture derivations with the original bands, the classes were further separated. The original image was also lowered in resolution, from 2.5m to 25m in pixel size. However, this did not generate either higher or similar overall accuracy compared to any of the high spatial resolution classified images. The moderate spatial resolution classifications were only computed with the MLC and SVM due to the inefficiency of an object based image analysis method when used with moderate spatial resolution. Of these six classifications, only two exceeded the 85% threshold of an acceptable overall accuracy. These were the SVM (86.8%) and OB-kNN (86.2%), which included the texture analysis. None of those classifications with only the three original bands exceeded this threshold. In conclusion, the object based method is the most suitable approach for this dataset because: 1) the parameter optimization is less subjective, 2) computational time is relatively lower, 3) the classes in the image are more cohesive and 4) there is less need for post-classification filtering.Människor boende i Brasiliens regnskogar livnär sig på svedjebruk, vilket är en jordbruksmetod där en först hugger ned skogen för att sen bränna resterande stubbar och annan vegetation. Jordbruksmetoden är, enligt SKY Rainforest Rescue, en ohållbar metod som kan försämra regnskogens ekosystem och därmed dess ekosystemtjänster som människan har kommit att bli beroende av. Organisationen arbetar för att invånarna ska lära sig att bruka en mer hållbar metod och för att övervaka utvecklingen av projektet använder sig SKY Rainforest Rescue av fjärranalys. Med hjälp av satellitbilder kan jordens yta studeras från ett avstånd vilket genererar en god överblick av ett större område vilket kan vara att föredra i den här studien. Analyserna utgår från bilder tagna av sensorer som är placerade på satelliter, vilka kretsar kring jorden i en omloppsbana och samtidigt registrerar bilder. Varje bild består av ett visst antal band där varje band representerar ett spektralt intervall t.ex. synligt ljus som grön, röd och blå, i det elektromagnetiska spektrumet. Högupplösta bilder är ett resultat av ny teknik som kommit ut på marknaden och det har med den utvecklingen uppstått frågor om hur en ska behandla satellitbilder i framtiden. Därför är det viktigt att utvärdera och utveckla metoder för bildbehandling. I den här studien används satellitbilder som är högupplösta, där en pixel motsvarar 2.5x2.5m på jordytan. Tre olika metoder används för att framställa markanvändningskartor för att finna den mest optimala metoden för just den plasten och typ av bild. Metoderna är klassificeringsmetoder som grundar sig på pixlars digitala nummer, en pixel kan ha ett värde mellan 0-255 där varje nummer representerar en färg. Två av dessa är baserade på varje pixels enskilda spektrala värden, den tredje segmenterar ihop närliggande pixlar med liknande värden till objekt och beräknar ett spektralt medelvärde av pixlarna tillhörande objekten. En stor skillnad mellan de två metoderna är att i den objektbaserade spelar en pixels intilliggande pixlar en stor roll, medan en pixelbaserad metod behandlar varje pixel enskilt oberoende utav grannpixlar. I och med högupplösta bilder kan intill liggande pixlar spela en större roll eftersom objekt t.ex. ett träd kan bestå av flera pixlar med varierande spektrala värden. En metod som kan minska det problem som uppstår är att analysera en bilds textur, alltså variationen av gråtoner i en bild. En markanvändningskarta måste valideras innan den kan accepteras som riktig. Validering är baserad på att jämföra stickprov från kartan med den faktiska marken och på det viset skatta hur bra kartan stämmer överens med verkligheten. Enligt tidigare studier ska den generella procenten av korrekt karterade punkter överstiga 85 % för att kartan i fråga ska accepteras som riktig och representativ för området. I studien framställs sex kartor, baserat på olika metoder från en högupplöst satellitbild och två kartor från samma bild men med lägre upplösning. Endast två av de åtta kartorna hade högre än 85 % korrekt karterade markanvändningsklasser. Den ena är baserad på enskilda pixlar (86.8%) och den andra är baserad på segmenterade pixlar (86.2%), vad metoderna har gemensamt är att de både inkluderar en texturanalys. Den objektbaserad är dock att föredra på grund av mindre komplex algoritm, mindre tidskrävande och ser visuellt bättre ut

Regional mapping of crops under agricultural nets using Sentinel-2

Geography and Environmental Studie

Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms