Chandi Witharana

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adelie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

Evaluating remote sensing image fusion algorithms for use in humanitarian crisis management

This study investigated how different fusion algorithms performed when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The selected algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings representing natural and anthropogenic crises that had occurred in the recent past: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka. Spectral quality metrics included correlation coefficient, peak signal-to-noise ratio index, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence and high-pass correlation coefficient. Under each metric, fusion methods were ranked and best competitors were identified. In this study, the Ehlers fusion, wavelet-PCA fusion (WVPCA), and the high-pass filter fusion algorithms reported the best values for the majority of spectral quality indices. Under spatial metrics, the University of New Brunswick and Gram-Schmidt fusion algorithms reported the optimum values. The color normalization sharpening and subtractive resolution merge algorithms exhibited the highest spectral distortions where as the WV-PCA algorithm showed the weakest spatial improvement. In conclusion, we recommend the University of New Brunswick algorithm if visual image interpretation is involved, whereas the high-pass filter fusion is recommended if semi- or fully-automated feature extraction is involved, for pansharpening VHSR satellite images of ongoing and post crisis sites

Assessing the spatial fidelity of resolution-enhanced imagery using Fourier analysis: a proof-of-concept study

Pan-sharpening of moderate resolution multispectral remote sensing data with those of a higher spatial resolution is a standard practice in remote sensing image processing. This paper suggests a method by which the spatial properties of resolution merge products can be assessed. Whereas there are several accepted metrics, such as correlation and root mean square error, for quantifying the spectral integrity of fused images, relative to the original multispectral data, there is less agreement on a means by which to assess the spatial properties, relative to the original higher-resolution, pansharpening data. In addition to qualitative, visual, and somewhat subjective evaluation, quantitative measures used have included correlations between high-pass filtered panchromatic and fused images, gradient analysis, wavelet analysis, among others. None of these methods, however, fully exploits the spatial and structural information contained in the original high resolution and fused images. This paper proposes the use of the Fourier transform as a means to quantify the degree to which a fused image preserves the spatial properties of the pan-sharpening high resolution data. A highresolution 8-bit panchromatic image was altered to produce a set of nine different test images, as well as a random image. The Fourier Magnitude (FM) image was calculated for each of the datasets and compared via FM to FM image correlation. Furthermore, the following edge detection algorithms were applied to the original and altered images: (a) Canny; (b) Sobel; and (c) Laplacian. These edge-filtered images were compared, again by way of correlation, with the original edge-filtered panchromatic image. Results indicate that the proposed method of using FTMI as a means of assessing the spatial fidelity of high-resolution imagery used in the data fusion process outperforms the correlations produced by way of comparing edge-enhanced images.

Who does what where? Advanced earth observation for humanitarian crisis management

This study investigated the performances of data fusion algorithms when applied to very high spatial resolution satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The candidate algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings representing natural and anthropogenic crises that had occurred in the recent past: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas in Sri Lanka. Fused images were assessed subjectively and objectively. Spectral quality metrics included correlation coefficient, peak signal-to-noise ratio index, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence and high-pass correlation coefficient. Under each metric, fusion methods were ranked and best competitors were identified. In this study, The Ehlers fusion, wavelet principle component analysis (WV-PCA) fusion, and the high-pass filter fusion algorithms reported the best values for the majority of spectral quality indices. Under spatial metrics, the University of New Brunswick and Gram-Schmidt fusion algorithms reported the optimum values. The color normalization sharpening and subtractive resolution merge algorithms exhibited the highest spectral distortions where as the WV-PCA algorithm showed the weakest spatial improvement. In conclusion, this study recommends the University of New Brunswick algorithm if visual image interpretation is involved, whereas the high-pass filter fusion is recommended if semi- or fully-automated feature extraction is involved, for pansharpening VHSR satellite images of on-going and post crisis sites.

Using LiDAR and GEOBIA for automated extraction of eighteenth–late nineteenth century relict charcoal hearths in southern New England

Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in dense tree canopies enabling archeologists to discover “hidden” past settlements and anthropogenic landscape features. While LiDAR has been increasingly adopted in archeological studies worldwide, its full potential is still being explored in the United States. Furthermore, while hand-digitizing features in remote-sensing datasets remain a valuable method for archeological surveys, it is often time- and labor intensive. The central objective of this research is to develop a geographic object-based image analysis-driven methodological framework linking low-level features and domain knowledge to automatically extract targets of interest from LiDAR-based digital terrain models (DTMs) and to closely examine the degree of interoperability of knowledge-based rulesets across different study sites focusing on the same semantic class. We apply this framework in southern New England, a geographic region in the northeastern United States where numerous seventeenth-century to early twentieth-century features such as relict charcoal hearths (RCHs), stone walls, and building foundations lie abandoned in densely forested terrain. Focusing on RCHs in this study, our results show promising agreement between manual and automated detection of these features. Overall, we show that the use of LiDAR data augmented with object-based classification workflows provides valuable baseline data for future archeological study and reconstruction of land-use/land-cover change over the past 300 years.