Carl Salvaggio

Radiometric scene normalization using pseudoinvariant features

Abstract A scene-to-scene radiometric normalization technique has been developed which corrects for atmospheric degradations, illumination effects, and sensor response differences in multitemporal multispectral imagery. The technique is based on the statistical invariance of the reflectance of man-made in-scene elements such as concrete, asphalt, and rooftops. Differences in the grey-level distributions of these invariant objects is assumed to be a linear function and is corrected statistically to perform the normalization. The technique exhibits errors in reflectance of approximately 1% for Landsat TM and high-resolution air photo imagery in all spectral regions studied.

Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds

A fast, completely automated method to create 3D watertight building models from airborne LiDAR point clouds is presented. The proposed method analyzes the scene content and produces multi-layer rooftops with complex boundaries and vertical walls that connect rooftops to the ground. A graph cuts based method is used to segment vegetative areas from the rest of scene content. The ground terrain and building rooftop patches are then extracted utilizing our technique, the hierarchical Euclidean clustering. Our method adopts a “divide-and-conquer” strategy. Once potential points on rooftops are segmented from terrain and vegetative areas, the whole scene is divided into individual pendent processing units which represent potential building footprints. For each individual building region, significant features on the rooftop are further detected using a specifically designed region growing algorithm with smoothness constraint. Boundaries for all of these features are refined in order to produce strict description. After this refinement, mesh models could be generated using an existing robust dual contouring method.

Methodologies and protocols for the collection of midwave and longwave infrared emissivity spectra using a portable field spectrometer

The development of highly portable field devices for measuring midwave and longwave infrared emissivity spectra has greatly enhanced the ability of scientists to develop and verify exploitation algorithms designed to operate in these spectral regions. These data, however, need to be collected properly in order to prove useful once the scientists return from the field. Attention to the removal of environmental factors such as reflected downwelling atmospheric and background radiance from the measured signal are of paramount importance. Proper separation of temperature and spectral emissivity is also a key factor in obtaining spectra of accurate shape and magnitude. A complete description of the physics governing the collection of field spectral emissivity data will be presented along with the assumptions necessary to obtain useful sample signatures. A detailed look at an example field collection device will be presented and the limitations and considerations when using such a device will be scrutinized. Attention will be drawn to the quality that can be expected from field measurements obtained and the limitations in their use that must be endured.

Incorporation of a time-dependent thermodynamic model and a radiation propagation model into infrared three-dimensional synthetic image generation

A model is presented for generation of synthetic images representing what an airborne or satellite thermal infrared imaging sensor would record. The scene and the atmosphere are modeled spectrally with final bandwidth determined by integration over the spectral bandwidth of the sensor (the model will function from 0.25 to 20 μm). The scene is created using a computer-aided-design package to create objects, assign attributes to facets, and assemble the scene. Object temperatures are computed using a thermodynamic model incorporating 24-h worth of meteorological history, as well as pixel specific solar load (i.e., self-shadowing is fully supported). The radiance reaching the sensor is computed using a ray tracer and atmospheric propagation models that vary with wavelength and slant range. Objects can be modeled as specular or diffuse with emissivities (reflectivities) dependent on look angle and wavelength. The resulting images mimic the phenomenology commonly observed by high-resolution thermal infrared sensors to a point where the model can be used as a research tool to evaluate the limitations in our understanding of the thermal infrared imaging process.

Integrated daylight harvesting and occupancy detection using digital imaging

This paper describes a proof-of-concept implementation that uses a high dynamic range CMOS video camera to integrate daylight harvesting and occupancy sensing functionalities. It has been demonstrated that the proposed concept not only circumvents several drawbacks of conventional lighting control sensors, but also offers functionalities that are not currently achievable by these sensors. The prototype involves three algorithms, daylight estimation, occupancy detection and lighting control. The calibrated system directly estimates luminance from digital images of the occupied room for use in the daylight estimation algorithm. A novel occupancy detection algorithm involving color processing in YCC space has been developed. Our lighting control algorithm is based on the least squares technique. Results of a daylong pilot test show that the system i) can meet different target light-level requirements for different task areas within the field-of-view of the sensor, ii) is unaffected by direct sunlight or a direct view of a light source, iii) detects very small movements within the room, and iv) allows real-time energy monitoring and performance analysis. A discussion of the drawbacks of the current prototype is included along with the technological challenges that will be addressed in the next phase of our research.

Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination

Immediately following the 12 January 2010 earthquake in Haiti, a disaster response team from Rochester Institute of Technology, ImageCat Inc., and Kucera International, funded by the Global Facility for Disaster Reduction and Recovery group of the World Bank, collected 0.15 m airborne imagery and two points/m2 lidar data for 650 km2 over a period of seven days. Data were transferred to Rochester, New York for processing at rates that approached 400 Mb/s using Internet2, ortho-rectified with a 24-hour turnaround, and distributed to response agencies through file or disk transfer. A unique response effort, dubbed the Global Earth Observation - Catastrophe Assessment Network (GEO-CAN) and headed by ImageCat, utilized over 600 experts from 23 different countries to generate rapid turnaround damage assessment products. This paper highlights the airborne data collection, transfer, processing, and product development effort, which arguably has raised the bar in terms of response to large-scale disasters.

DETECTION OF GASEOUS EFFLUENTS FROM AIRBORNE LWIR HYPERSPECTRAL IMAGERY USING PHYSICS-BASED SIGNATURES

Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and the concentration of the gas. All of these quantities vary spatially further complicating the detection problem. In complex scenes simple estimation of a “residual” spectrum may not be possible due to the variability in the scene background. A common detection scheme uses a matched filter formalism to compare laboratory-measured gas absorption spectra with measured pixel radiances. This methodology can not account for the variable signature strengths due to concentration path length and temperature contrast, nor does it take into account measured signatures that are observed in both absorption and emission in the same scene. We have developed a physics-based, forward model to predict in-scene signatures covering a wide range in gas / surface properties. This target space is reduced to a set of basis vectors using a geometrical model of the space. Corresponding background basis vectors are derived to describe the non-plume pixels in the image. A Generalized Likelihood Ratio Test is then used to discriminate between plume and non-plume pixels. Several species can be tested for iteratively. The algorithm is applied to airborne LWIR hyperspectral imagery collected by the Airborne Hyperspectral Imager (AHI) over a chemical facility with some ground truth. When compared to results from a clutter matched filter the physics-based signature approach shows significantly improved performance for the data set considered here.

Quantitative analysis of infrared contrast enhancement algorithms

Dynamic range reduction and contrast enhancement are two image-processing methods that are required when developing thermal camera systems. The two methods must be performed in such a way that the high dynamic range imagery output from current sensors are compressed in a pleasing way for display on lower dynamic range monitors. This research examines a quantitative analysis of infrared contrast enhancement algorithms found in literature and developed by the author. Four algorithms were studied, three of which were found in literature and one developed by the author: tail-less plateau equalization (TPE), adaptive plateau equalization (APE), the method according to Aare Mällo (MEAM), and infrared multi-scale retinex (IMSR). TPE and APE are histogram-based methods, requiring the calculation of the probability density of digital counts within an image. MEAM and IMSR are frequency-domain methods, methods that operate on input imagery that has been split into components containing differing spatial frequency content. After a rate of growth analysis and psychophysical trial were performed, MEAM was found to be the best algorithm.

Identification and detection of gaseous effluents from hyperspectral imagery using invariant algorithms

The ability to detect and identify effluent gases is, and will continue to be, of great importance. This would not only aid in the regulation of pollutants but also in treaty enforcement and monitoring the production of weapons. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research utilizes hyperspectral imagery in the infrared region of the electromagnetic spectrum to evaluate an invariant method of detecting and identifying gases within a scene. The image is evaluated on a pixel-by-pixel basis and is studied at the subpixel level. A library of target gas spectra is generated using a simple slab radiance model. This results in a more robust description of gas spectra which are representative of real-world observations. This library is the subspace utilized by the detection and identification algorithms. The subspace will be evaluated for the set of basis vectors that best span the subspace. The Lee algorithm will be used to determine the set of basis vectors, which implements the Maximum Distance Method (MaxD). A Generalized Likelihood Ratio Test (GLRT) determines whether or not the pixel contains the target. The target can be either a single species or a combination of gases. Synthetically generated scenes will be used for this research. This work evaluates whether the Lee invariant algorithm will be effective in the gas detection and identification problem.

Incorporation of texture in multispectral synthetic image generation tools

The digital imaging and remote sensing synthetic image generation (DIRSIG) model emphasizes quantitative prediction of the radiance reaching sensors with bandpass values between 0.28 and 20.0 micrometers . The model embodies a rigorous end-to-end spectral modeling of radiation propagation, absorption and scattering, target temperatures based on meteorological history, extensive directional target-background interactions, and detector responsivities. This paper describes texture quantification, the spectral-spatial correlation of textures, texture collection and generation methods. Finally, we describe how DIRSIG generates texture on a pixel by pixel basis and maintains the spectral correlation of targets between bands.