A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region

Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images

With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of remotely sensed images. However, the exploration of these images still involves a tremendous amount of human interventions, which are tedious, time-consuming, and inefficient. To help imaging experts gain a complete understanding of the images and locate the objects of interest in a more accurate and efficient way, there is always an urgent need for developing automatic detection algorithms. In this work, we delve into the object detection problems in remote sensing applications, exploring the detection algorithms for both hyperspectral images (HSIs) and high resolution aerial images. In the first part, we focus on the subpixel target detection problem in HSIs with low spatial resolutions, where the objects of interest are much smaller than the image pixel spatial resolution. To this end, we explore the detection frameworks that integrate image segmentation techniques in designing the matched filters (MFs). In particular, we propose a novel image segmentation algorithm to identify the spatial-spectral coherent image regions, from which the background statistics were estimated for deriving the MFs. Extensive experimental studies were carried out to demonstrate the advantages of the proposed subpixel target detection framework. Our studies show the superiority of the approach when comparing to state-of-the-art methods. The second part of the thesis explores the object based image analysis (OBIA) framework for geospatial object detection in high resolution aerial images. Specifically, we generate a tree representation of the aerial images from the output of hierarchical image segmentation algorithms and reformulate the object detection problem into a tree matching task. We then proposed two tree-matching algorithms for the object detection framework. We demonstrate the efficiency and effectiveness of the proposed tree-matching based object detection framework. In the third part, we study object detection in high resolution aerial images from a machine learning perspective. We investigate both traditional machine learning based framework and end-to-end convolutional neural network (CNN) based approach for various object detection tasks. In the traditional detection framework, we propose to apply the Gaussian process classifier (GPC) to train an object detector and demonstrate the advantages of the probabilistic classification algorithm. In the CNN based approach, we proposed a novel scale transfer module that generates enhanced feature maps for object detection. Our results show the efficiency and competitiveness of the proposed algorithms when compared to state-of-the-art counterparts

Uncertainty Assessment of Spectral Mixture Analysis in Remote Sensing Imagery

Spectral mixture analysis (SMA), a scheme of sub-pixel-based classifications, is one of the widely used models to map fractional land use and land cover information in remote sensing imagery. It assumes that: 1) a mixed pixel is composed by several pure land cover classes (endmembers) linearly or nonlinearly, and 2) the spectral signature of each endmember is a constant within the entire spatial extent of analysis. SMA has been commonly applied to impervious surface area extraction, vegetation fraction estimation, and land use and land cover change (LULC) mapping. Limitations of SMA, however, still exist. First, the existence of between- and within-class variability prevents the selection of accurate endmembers, which results in poor accuracy of fractional land cover estimates. Weighted spectral mixture analysis (WSMA) and transformed spectral mixture analysis (TSMA) are alternate means to address the within- and between- class variability. These methods, however, have not been analyzed systematically and comprehensively. The effectiveness of each WSMA and TSMA scheme is still unknown, in particular within different urban areas. Second, multiple endmember SMA (MESMA) is a better alternative to address spectral mixture model uncertainties. It, nonetheless, is time consuming and inefficient. Further, incorrect endmember selections may still limit model performance as the best-fit endmember model might not be the optimal model due to the existence of spectral variability. Therefore, this study aims 1) to explore endmember uncertainties by examining WSMA and TSMA modeling comprehensively, and 2) to develop an improved MESMA model in order to address the uncertainties of spectral mixture models. Results of the WSMA examination illustrated that some weighting schemes did reduce endmember uncertainties since they could improve the fractional estimates significantly. The results also indicated that spectral class variance played a key role in addressing the endmember uncertainties, as the better performing weighting schemes were constructed with spectral class variance. In addition, the results of TSMA examination demonstrated that some TSMAs, such as normalized spectral mixture analysis (NSMA), could effectively solve the endmember uncertainties because of their stable performance in different study areas. Results of Class-based MEMSA (C-MESMA) indicated that it could address spectral mixture model uncertainties by reducing a lot of the calculation burden and effectively improving accuracy. Assessment demonstrated that C-MEMSA significantly improving accuracy. Major contributions of this study can be summarized as follow. First, the effectiveness of addressing endmember uncertainties have been fully discussed by examining: 1) the effectiveness of ten weighted spectral mixture models in urban environments; and 2) the effectiveness of 26 transformed spectral mixture models in three locations. Constructive guidance regarding handling endmember uncertainties using WSMA and TSMA have been provided. Second, the uncertainties of spectral mixture model were reduced by developing an improved MESMA model, named C-MESMA. C-MESMA could restrict the distribution of endmembers and reduce the calculation burden of traditional MESMA, increasing SMA accuracy significantly

Experimental and simulation study results for video landmark acquisition and tracking technology

A synopsis of related Earth observation technology is provided and includes surface-feature tracking, generic feature classification and landmark identification, and navigation by multicolor correlation. With the advent of the Space Shuttle era, the NASA role takes on new significance in that one can now conceive of dedicated Earth resources missions. Space Shuttle also provides a unique test bed for evaluating advanced sensor technology like that described in this report. As a result of this type of rationale, the FILE OSTA-1 Shuttle experiment, which grew out of the Video Landmark Acquisition and Tracking (VILAT) activity, was developed and is described in this report along with the relevant tradeoffs. In addition, a synopsis of FILE computer simulation activity is included. This synopsis relates to future required capabilities such as landmark registration, reacquisition, and tracking

Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

The hyperspectral image analysis technique one of the most advanced remote sensing tools has been used as a possible means of identifying from a single pixel or in the field of view of the sensor An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel endmember and a set of corresponding fractions of the spectral signature in the pixel abundances and this problem is known as un-mixing The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation extracted from the spectral endmember selection procedures including the reflectance calibration of Landsat ETM image using ENVI software minimum noise fraction MNF pixel purity index PPI and n-dimensional visualization The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene The endmember extraction tendency to the type of endmembers being derived and the number of endmembers estimated by an algorithm with respect to the number of spectral bands and the number of pixels being processed also the required input data and the kind of noise if any in the signal model surveying done Results of the present study using the hyperspectral image analysis technique ascertain that Landsat ETM data can be used to generate valuable vegetative information for the District Vehari Punjab Province Pakista

Tree species identification in an urban environment using a data fusion approach

This thesis explores a data fusion approach combining hyperspectral, LiDAR, and multispectral data to classify tree species in an urban environment. The study area is the campus of the University of Northern Iowa. In order to use the data fusion approach, a wide variety of data was incorporated into the classification. These data include: a four-band Quickbird image from April 2003 with 0.6m spatial resolution, a 24-band AISA hyperspectral image from July 2004 with 2m spatial resolution, a 63-band AISA Eagle hyperspectral image from October 2006 with lm spatial resolution, a high resolution, multiple return LiDAR data set from April 2006 with sub-meter posting density, spectrometer data gathered in the field, and a database containing the location and type of every tree in the study area. The elevation data provided by the LiDAR was fused with the imagery in eCognition Professional. The LiDAR data was used to refine class rules by defining trees as objects with elevation greater than 3 meters. Classes included honey locust, white pine, crab apple, sugar maple, white spruce, American basswood, pin oak and ash. Results indicate fusing LiDAR data with these imageries showed an increase in overall classification accuracy for all datasets. Overall classification accuracy with the October 2006 hyperspectral data and LiDAR was 93%. Increases in overall accuracy ranged from 12 to 24% over classifications based on spectral imagery alone. Further, in this study, hyperspectral data with higher spatial resolution provided increased classification accuracy. The limitations of the study included a LiDAR data set that was acquired slightly before the leaves had matured. This affected the shape and extent of these trees based on their LiDAR returns. The July 2004 hyperspectral data set was difficult to georectify with its 2m resolution. This may have resulted in some minor issues of alignment between the LiDAR and the July 2004 hyperspectral data. Future directions of the study include developing a classification scheme using a Classification And Regression Tree, utilizing all of the LiDAR returns in a classification instead of just the first and fourth returns, and examining an additional LiDAR-derived data set with estimated tree locations

Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery

Many urban areas are using estimations of impervious surfaces as a means for better environmental management. This is because research over the last two decades indicate a consistent, inverse relationship between the percentage of impervious surfaces in a watershed and the environmental problems urban areas are experiencing. Although various methods for estimating impervious surfaces can be identified, few result in accurate and defensible estimations by which environmental problems can be assessed. This is especially important to rapidly expanding urban areas such as Baton Rouge, Louisiana where detailed records and planimetric data are lacking. Numerous studies have shown a potential for estimating impervious surfaces using remotely sensed satellite imagery however, none were performed in a sub-tropical geographical area such as southern Louisiana. Three different dates of Landsat TM multi-spectral imagery, corresponding to seasonal differences, were acquired for land cover type classification purposes. Seasonal dates of imagery were used to determine tree canopy effects and the optimum season for estimating impervious surfaces from satellite imagery. Unique to this study, the derived classified estimates were compared to an impervious surfaces reference estimate developed from high resolution, true color aerial photography. The impervious surfaces reference estimate was developed by digitizing over 15,000 polygons of impervious features throughout the watershed such as roads, buildings, and parking lots. Statistical evaluation of the seasonal classified images included the error matrix analysis, Kappa analysis (both overall and conditional), and the Pair-Wise Z test statistic. Results obtained in this research indicate overall accuracies of the derived classified estimates ranged between 75.33 percent and 81.33 percent while differing from the reference estimate by 10 percent or less

Spectral image utility for target detection applications

In a wide range of applications, images convey useful information about scenes. The “utility” of an image is defined with reference to the specific task that an observer seeks to accomplish, and differs from the “fidelity” of the image, which seeks to capture the ability of the image to represent the true nature of the scene. In remote sensing of the earth, various means of characterizing the utility of satellite and airborne imagery have evolved over the years. Recent advances in the imaging modality of spectral imaging have enabled synoptic views of the earth at many finely sampled wavelengths over a broad spectral band. These advances challenge the ability of traditional earth observation image utility metrics to describe the rich information content of spectral images. Traditional approaches to image utility that are based on overhead panchromatic image interpretability by a human observer are not applicable to spectral imagery, which requires automated processing. This research establishes the context for spectral image utility by reviewing traditional approaches and current methods for describing spectral image utility. It proposes a new approach to assessing and predicting spectral image utility for the specific application of target detection. We develop a novel approach to assessing the utility of any spectral image using the target-implant method. This method is not limited by the requirements of traditional target detection performance assessment, which need ground truth and an adequate number of target pixels in the scene. The flexibility of this approach is demonstrated by assessing the utility of a wide range of real and simulated spectral imagery over a variety ii of target detection scenarios. The assessed image utility may be summarized to any desired level of specificity based on the image analysis requirements. We also present an approach to predicting spectral image utility that derives statistical parameters directly from an image and uses them to model target detection algorithm output. The image-derived predicted utility is directly comparable to the assessed utility and the accuracy of prediction is shown to improve with statistical models that capture the non-Gaussian behavior of real spectral image target detection algorithm outputs. The sensitivity of the proposed spectral image utility metric to various image chain parameters is examined in detail, revealing characteristics, requirements, and limitations that provide insight into the relative importance of parameters in the image utility. The results of these investigations lead to a better understanding of spectral image information vis-à-vis target detection performance that will hopefully prove useful to the spectral imagery analysis community and represent a step towards quantifying the ability of a spectral image to satisfy information exploitation requirements

Multiclass Object Detection with Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object\u27s spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches