A Study of Feature Extraction Using Divergence Analysis of Texture Features

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Development of Landsat-based Technology for Crop Inventories: Appendices

There are no author-identified significant results in this report

Assessing remote sensing application on rangeland insurance in Canadian prairies

Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour I’Observation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented

Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)

Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.info:eu-repo/semantics/publishedVersio

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

Developing a method to map coconut agrosystems from high-resolution satellite images

https://icaci.org/files/documents/ICC_proceedings/ICC2015/papers/38/fullpaper/T38-504_1427765394.pdfInternational audienceOur study aims at developing a generalizable method to exploit high resolution satellite images(VHR) for mapping coconut-based agrosystems, differentiating them from oil palm agrosystems.We compared two methods of land use classification. The first one is similar to that described byTeina (2009), based on spectral analysis and watershed segmentation, which we simplified byusing the NDVI vegetation index. The second one is the semi-automatic classification based ontexture analysis (PAPRI method of Borne, 1990). These methods were tested in two differentenvironments: Vanua Lava (Vanuatu; heterogeneous landscape, very ancient plantations) andIvory Coast (Marc Delorme Research Station, monoculture, regular spacing, oil palm plantations);and their results were evaluated against manually digitized photo-interpretation maps.In both situations, the PAPRI method produced better results than that of Teina (global kappa of0.60 vs. 0.40). Spectral signatures do not allow a sufficiently accurate mapping of coconut and donot differentiate it from oil palm, despite their different NDVI signatures. The PAPRI methoddifferentiates productive coconut from mixed plantations and other vegetation, either high or low(70% accuracy). In both situations, Teina’s method allows counting 65% of the coconut treeswhen they are well spaced. To increase the method accuracy, we suggest (1) field surveys (forsmall scale studies) and/or finer image resolution, allowing a high precision in manual mappingwith a better discrimination between coconut and oil palm, thus limiting the proportion of mixedpixels. (2) A phenological monitoring could improve the distinction between coconut and oil palmagrosystems. (3) Hyper-spectral images should allow extracting more precisely the respectivesignatures of both species. Another possibility would be (4) an object-oriented analysis asproposed by the eCognition software. Finally, (5) coupling the Lidar system with watershedanalysis would allow a better characterization of coconut varietal types

Land use/cover change using Remote Sensing and Geographic Information Systems : Pic Macaya National Park, Haiti

A post classification change detection technique based on a hybrid classification approach (unsupervised and supervised) was applied to Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Plus (ETM+), and ASTER images acquired in 1987, 2000 and 2004 respectively to map land use/cover changes in the Pic Macaya National Park in the southern region of Haiti. Each image was classified individually into six land use/cover classes: built-up, agriculture, herbaceous, open pine forest, mixed forest, and barren land using unsupervised ISODATA and maximum likelihood supervised classifiers with the aid of field collected ground truth data collected in the field. Ground truth information, collected in the field in December 2007, and including equalized stratified random points which were visual interpreted were used to assess the accuracy of the classification results. The overall accuracy of the land classification for each image was respectively: 1987 (82%), 2000 (82%), 2004 (87%). A post classification change detection technique was used to produce change images for 1987 to 2000, 1987 to 2004, and 2000 to 2004. It was found that significant changes in the land use/cover occurred over the 17- year period. The results showed increases in built up (from 10% to 17%) and herbaceous (from 5% to 14%) areas between 1987 and 2004. The increase of herbaceous was mostly caused by the abandonment of exhausted agriculture lands. At the same time, open pine forest and mixed forest areas lost (75%) and (83%) of their area to other land use/cover types. Open pine forest (from 20% to 14%) and mixed forest (from18 to 12%) were transformed into agriculture area or barren land. This study illustrated the continuing deforestation, land degradation and soil erosion in the region, which in turn is leading to decrease in vegetative cover. The study also showed the importance of Remote Sensing (RS) and Geographic Information System (GIS) technologies to estimate timely changes in the land use/cover, and to evaluate their causes in order to design an ecological based management plan for the park

Improving Hyperspectral Subpixel Target Detection Using Hybrid Detection Space

A Hyper-Spectral Image (HSI) has high spectral and low spatial resolution. As a result, most targets exist as subpixels, which pose challenges in target detection. Moreover, limitation of target and background samples always hinders the target detection performance. In this thesis, a hybrid method for subpixel target detection of an HSI using minimal prior knowledge is developed. The Matched Filter (MF) and Adaptive Cosine Estimator (ACE) are two popular algorithms in HSI target detection. They have different advantages in differentiating target from background. In the proposed method, the scores of MF and ACE algorithms are used to construct a hybrid detection space. First, some high abundance target spectra are randomly picked from the scene to perform initial detection to determine the target and background subsets. Then, the reference target spectrum and background covariance matrix are improved iteratively, using the hybrid detection space. As the iterations continue, the reference target spectrum gets closer and closer to the central line that connects the centers of target and background and resulting in noticeable improvement in target detection. Two synthetic datasets and two real datasets are used in the experiments. The results are evaluated based on the mean detection rate, Receiver Operating Characteristic (ROC) curve and observation of the detection results. Compared to traditional MF and ACE algorithms with Reed-Xiaoli Detector (RXD) background covariance matrix estimation, the new method shows much better performance on all four datasets. This method can be applied in environmental monitoring, mineral detection, as well as oceanography and forestry reconnaissance to search for extremely small target distribution in a large scene