KEYWORDS: Clouds, Image segmentation, Prototyping, Image classification, Infrared imaging, Long wavelength infrared, Signal to noise ratio, Satellites, Thermography, Algorithm development
This paper reports on a novel approach to atmospheric cloud segmentation from a space based multi-spectral pushbroom satellite system. The satellite collects 15 spectral bands ranging from visible, 0.45 um, to long wave infa-red (IR), 10.7um. The images are radiometrically calibrated and have ground sample distances (GSD) of 5 meters for visible to very near IR bands and a GSD of 20 meters for near IR to long wave IR. The algorithm consists of a hybrid-classification system in the sense that supervised and unsupervised networks are used in conjunction. For performance evaluation, a series of numerical comparisons to human derived cloud borders were performed. A set of 33 scenes were selected to represent various climate zones with different land cover from around the world. The algorithm consisted of the following. Band separation was performed to find the band combinations which form significant separation between cloud and background classes. The potential bands are fed into a K-Means clustering algorithm in order to identify areas in the image which have similar centroids. Each cluster is then compared to the cloud and background prototypes using the Jeffries-Matusita distance. A minimum distance is found and each unknown cluster is assigned to their appropriate prototype. A classification rate of 88% was found when using one short wave IR band and one mid-wave IR band. Past investigators have reported segmentation accuracies ranging from 67% to 80%, many of which require human intervention. A sensitivity of 75% and specificity of 90% were reported as well.
The Multispectral Thermal Imager Satellite (MTI), launched on March 12, 2000, is a multispectral pushbroom system that acquires 15 unique spectral bands of data from 0.45-10.7 microns, with resolutions of 5 m for the visible bands and 20 m for the infrared. Scene data are collected on three separate sensor chip assemblies (SCAs) mounted on the focal plane. The process of image registration for MTI satellite imagery therefore requires two separate steps: (1) the multispectral data collected by each SCA must be coregistered and (2) the SCAs must be registered with respect to each other. An automated algorithm was developed to register the MTI imagery. This algorithm performs a phase correlation on edge-maps generated from paired bands of data and then spatial-filters the result to calculate the relative shifts between bands. The process is repeated on every combination of band pairs to generate a vector of coregistration results for each SCA. The three SCAs are then registered to each other using a similar process operating on just one spectral band. The resulting registration values are used to produce a linearly shifted un-resampled coregistered image cube. This study shows the results of 791 image registration attempts using the EdgeReg registration code and compares them to a perfect reference data set of the same images registered manually.
The Multispectral Thermal Imager Satellite (MTI) has been used to test a sub-pixel sampling technique in an effort to obtain higher spatial frequency imagery than that of its original design. The MTI instrument is of particular interest because of its infrared detectors. In this spectral region, the detector size is traditionally the limiting factor in determining the satellite’s ground sampling distance (GSD). Additionally, many over-sampling techniques require flexible command and control of the sensor and spacecraft. The MTI sensor is well suited for this task, as it is the only imaging system on the MTI satellite bus. In this super-sampling technique, MTI is maneuvered such that the data are collected at sub-pixel intervals on the ground. The data are then processed using a deconvolution algorithm using in-scene measured point spread functions (PSF) to produce an image with synthetically-boosted GSD.
The Digital Elevation Model (DEM) extraction process traditionally uses a stereo pair of aerial photographs that are sequentially captured using an airborne metric camera. Standard DEM extraction techniques have been naturally extended to utilize satellite imagery. However, the particular characteristics of satellite imaging can cause difficulties in the DEM extraction process. The ephemeris of the spacecraft during the collects, with respect to the ground test site, is the most important factor in the elevation extraction process. When the angle of separation between the stereo images is small, the extraction process typically produces measurements with low accuracy. A large angle of separation can cause an excessive number of erroneous points in the output DEM. There is also a possibility of having occluded areas in the images when drastic topographic variation is present, making it impossible to calculate elevation in the blind spots. The use of three or more images registered to the same ground area can potentially reduce these problems and improve the accuracy of the extracted DEM. The pointing capability of the Multispectral Thermal Imager (MTI) allows for multiple collects of the same area to be taken from different perspectives. This functionality of MTI makes it a good candidate for the implementation of DEM extraction using multiple images for improved accuracy. This paper describes a project to evaluate this capability and the algorithms used to extract DEMs from multi-look MTI imagery.
The Multispectral Thermal Imager (MTI) provides a highly informative source of remote sensing data. However, the analysis and exploitation can be very challenging. Effective utilization of this imagery by an image analyst typically requires a consistent and timely means of locating regions of interest. Many available image analysis/segmentation techniques are often slow, not robust to spectral variabilities from view to view or within a spectrally similar region, and/or require a significant amount of user intervention to achieve a segmentation corresponding to self-similar regions within the data. This paper discusses a segmentation approach that exploits the gross spectral shape of MTI data. In particular, we propose a nonparametric approach to perform coarse level segmentation that can stand alone or as a potential precursor to other image analysis tools. In comparison to previous techniques, the key characteristics of this approach are in its simplicity, speed, and consistency. Most importantly it requires relatively few user inputs and determines the number of clusters, their extent, and, data assignment directly from the data.
A familiar concept in imaging spectrometry is that of the three dimensional data cube, with one spectral and two spatial dimensions. However, available detectors have at most two dimensions, which generally leads to the introduction of either scanning or multiplexing techniques for imaging spectrometers. For situations in which noise increases less rapidly than as the square root of the signal, multiplexing techniques have the potential to provide superior signal-to-noise ratios. This paper presents a theoretical description and numerical simulations for a new and simple type of Hadamard transform multiplexed imaging spectrometer. Compared to previous types of spatially encoded imaging spectrometers, it increases etendue by eliminating the need for anamorphically compressed re-imaging onto the entrance aperture of a monochromator or spectrophotometer. Compared to previous types of spectrally encoded imaging spectrometers, it increases end-to-end transmittance by eliminating the need for spectral re-combining optics. These simplifications are attained by treating the pixels of a digital mirror array as virtual entrance slits and the pixels of a 2-D array detector as virtual exit slits of an imaging spectrometer, and by applying a novel signal processing technique.
Interband coregistration of multispectral satellite imagery is essential to exploiting the spectral information inherent in these data. A semi-automatic image registration method has been developed for Multispectral Thermal Imager (MTI) data. This registration method, based on feature fitting within the image, is applicable to the 14 MTI spectral bands that contain ground information; these spectral bands range from 0.45 to 10.7micrometers . The feature fitting registration method requires selection of an appropriate scene feature in the image, usually a crossroad or other feature with moderately high contrast to compute the required shift in x and y for each band. This paper describes the algorithm and provides examples of images registered using this method. Preliminary results show that for MTI image registration, feature fitting yields better results than cross-correlation. Results also show that this algorithm works well for a broad variety of scenes; this algorithm has been applied to images with scene content ranging from desert images with very little structure to heavily forested images. This method has been developed in support of the MTI mission, but may easily be extended for use on image data collected by other multispectral sensors.
Hadamard Transform Spectrometer (HTS) approaches share the multiplexing advantages found in Fourier transform spectrometers. Interest in Hadamard systems has been limited due to data storage/computational limitations and the inability to perform accurate high order masking in a reasonable amount of time. Advances in digital micro-mirror array (DMA) technology have opened the door to implementing an HTS for a variety of applications including fluorescent microscope imaging and Raman imaging. A Hadamard transform spectral imager (HTSI) for remote sensing offers a variety of unique capabilities in one package such as variable spectral and temporal resolution, no moving parts (other than the micro-mirrors) and vibrational insensitivity. An HTSI for remote sensing using a Texas Instrument digital micro-mirror device (DMD) is being designed for use in the spectral region 1.25 - 2.5 micrometers . In an effort to optimize and characterize the system, an HTSI sensor system simulation has been concurrently developed. The design specifications and hardware components for the HTSI are presented together with results calculated by the HTSI simulation that include the effects of digital (vs. analog) scene data input, detector noise, DMD rejection ratios, multiple diffraction orders and multiple Hadamard mask orders.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.