Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.
In multitemporal very-high-resolution urban remote sensing images, buildings, especially high-rise buildings, show difference in terms of morphology due to the different view angles. In the coregistered images, the pixels of the same building are not corresponding to each other, which causes false alarm in change detection. Our objective is to find out the matching points located on the roofs of high-rise buildings. When the difference of view angle between the coregistered images is fixed, we discover that there are spatial translation relationships, i.e., local translation transformation and fixed angle offset, between the point matches of high-rise building roofs. Therefore, using these relationships, a method that can sift out point matches of roofs to their correct positions is proposed. The experimental results show that most point matches located on the roof of the same building can be fast and correctly sifted out.
Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.
Traditional surface water quality assessment is costly, labor intensive, and time consuming; however, remote sensing has the potential to assess surface water quality because of its spatiotemporal consistency. Therefore, estimating concentrations of surface water quality parameters (SWQPs) from satellite imagery is essential. Remote sensing estimation of nonoptical SWQPs, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), and dissolved oxygen (DO), has not yet been performed because they are less likely to affect signals measured by satellite sensors. However, concentrations of nonoptical variables may be correlated with optical variables, such as turbidity and total suspended sediments, which do affect the reflected radiation. In this context, an indirect relationship between satellite multispectral data and COD, BOD, and DO can be assumed. Therefore, this research attempts to develop an integrated Landsat 8 band ratios and stepwise regression to estimate concentrations of both optical and nonoptical SWQPs. Compared with previous studies, a significant correlation between Landsat 8 surface reflectance and concentrations of SWQPs was achieved and the obtained coefficient of determination (R2)>0.85. These findings demonstrated the possibility of using our technique to develop models to estimate concentrations of SWQPs and to generate spatiotemporal maps of SWQPs from Landsat 8 imagery.
Moving objects have been detected using various object detection techniques. Two categories for moving object detection techniques are frame differencing based and background subtraction based. These techniques are limited by camera scene complexity, light conditions, video type etc. Frame differencing based techniques process videos faster compared to background subtraction based techniques. Frame differencing based techniques detects only the boundary of the moving object and may fail for slow moving objects. These techniques for moving object detection can be improved by using sound data as most video recording cameras are equipped with a microphone. Sounds from human footsteps can be recorded with video and used with frame differencing techniques to improve moving object detection results. Camera microphones also record background noise with other background sound. This noisy data has been filtered out using the Fourier transform. When peak locations for each footstep sound are determined, and a Full Width at Half Maxima is computed for each peak, the number of frames within this width are counted, these frames are verify the presence of a moving object.
Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.
UNB Pan-sharp, also named FuzeGo, is an image fusion technique to produce high resolution color satellite images by
fusing a high resolution panchromatic (monochrome) image and a low resolution multispectral (color) image. This is an
effective solution that modern satellites have been using to capture high resolution color images at an ultra-high speed.
Initial research on security camera systems shows that the UNB Pan-sharp technique can also be utilized to produce high
resolution and high sensitive color video images for various imaging and monitoring applications. Based on UNB Pansharp
technique, a video camera prototype system, called the UNB Super-camera system, was developed that captures
high resolution panchromatic images and low resolution color images simultaneously, and produces real-time high
resolution color video images on the fly. In a separate study, it was proved that UNB Super Camera outperforms
conventional 1-chip and 3-chip color cameras in image quality, especially when the illumination is low such as in room
lighting. In this research the influence of image compression on the quality of UNB Pan-sharped high resolution color
images is evaluated, since image compression is widely used in still and video cameras to reduce data volume and speed
up data transfer. The results demonstrate that UNB Pan-sharp can consistently produce high resolution color images that
have the same detail as the input high resolution panchromatic image and the same color of the input low resolution color
image, regardless the compression ratio and lighting condition. In addition, the high resolution color images produced by
UNB Pan-sharp have higher sensitivity (signal to noise ratio) and better edge sharpness and color rendering than those of
the same generation 1-chip color camera, regardless the compression ratio and lighting condition.
A 2-chip color camera, named UNB Super-camera, is introduced in this paper. Its image qualities in different lighting
conditions are compared with those of a 1-chip color camera and a 3-chip color camera. The 2-chip color camera
contains a high resolution monochrome (panchromatic) sensor and a low resolution color sensor. The high resolution
color images of the 2-chip color camera are produced through an image fusion technique: UNB pan-sharp, also named
FuzeGo. This fusion technique has been widely used to produce high resolution color satellite images from a high
resolution panchromatic image and low resolution multispectral (color) image for a decade. Now, the fusion technique is
further extended to produce high resolution color still images and video images from a 2-chip color camera. The initial
quality assessments of a research project proved that the light sensitivity, image resolution and color quality of the
Super-camera (2-chip camera) is obviously better than those of the same generation 1-chip camera. It is also proven that
the image quality of the Super-camera is much better than the same generation 3-chip camera when the light is low, such
as in a normal room light condition or darker. However, the resolution of the Super-camera is the same as that of the 3-
chip camera, these evaluation results suggest the potential of using 2-chip camera to replace 3-chip camera for capturing
high quality color images, which is not only able to lower the cost of camera manufacture but also significantly
improving the light sensitivity.
Soil-moisture information plays an important role in disaster predictions, environmental
monitoring, and hydrological applications. A large number of research papers have
introduced a variety of methods to retrieve soil-moisture information from different types of
remote sensing data, such as optical data or radar data. We evaluate the most robust methods for
retrieving soil-moisture information of bare soil and vegetation-covered soil. We begin with an
introduction to the importance and challenges of soil-moisture information extraction and the
development of soil-moisture retrieval methods. An overview of soil-moisture retrieval methods
using different remote sensing data is presented-either active or passive or a combination of
both active and passive remote sensing data. The results of the methods are compared, and
the advantages and limitations of each method are summarized. The comparison shows that
using a statistical method gives the best results among others in the group: a combination of
both active and passive sensing methods, reaching a 1.83% gravimetric soil moisture (%GSM)
root-mean-square error (RMSE) and a 96% correlation between the estimated and field soil
measurements. In the group of active remote sensing methods, the best method is a backscatter
empirical model, which gives a 2.32-1.81%GSM RMSE and a 95-97% correlation between the
estimated and the field soil measurements. Finally, among the group of passive remote sensing
methods, a neural networks method gives the most desirable results: a 0.0937%GSM RMSE
and a 100% correlation between the estimated and field soil measurements. Overall, the newly
developed neural networks method with passive remote sensing data achieves the best results
among all the methods reviewed.
This paper presents a method on how to organize 3D remote sensing images and how to publish these images quickly.
We use two levels of grid-based spatial index to organize massive images. First, we divide a huge digital city image into
many map sheets (big images). All of map sheets construct a matrix structure. We use row number and column number
to encode every map sheet. Second, by using resample and bilinear interpolation method, we build pyramid for every
map sheet to form multi-scale hierarchical structure. At the same time building pyramid, we adopt JPEG compression
technology to produce JPEG image format files. The number of output image files equals to the number of pyramid
layers. Third, divide every pyramid layer image into many small image tiles. The size of each tile image is 256*256
pixels. All of small tiles of each pyramid layer image also construct a matrix structure. We also use row number and
column number to encode every small image tile. We create a file directory for each map sheet in order to store all of
small image tiles. we neatly combine the spatial index structure with the file name of each tile, which make server be
able to return tile to browser side very quickly without any query operation. With the proposed method, we can provide
users with a fast and efficiently tool to publish their own spatial information without involving any programming work.
The system performance is very good and the response time is almost identical for different size images.
KEYWORDS: Web services, Information fusion, Geographic information systems, Databases, Computing systems, Information technology, Interfaces, Image registration, Data modeling, Internet
Spatial information systems and spatial information in different geographic locations usually belong to different organizations. They are distributed and often heterogeneous and independent from each other. This leads to the fact that many isolated spatial information islands are formed, reducing the efficiency of information utilization. In order to address this issue, we present a method for effective spatial information integration based on web service. The method applies asynchronous invocation of web service and dynamic invocation of web service to implement distributed, parallel execution of web map services. All isolated information islands are connected by the dispatcher of web service and its registration database to form a uniform collaborative system. According to the web service registration database, the dispatcher of web services can dynamically invoke each web map service through an asynchronous delegating mechanism. All of the web map services can be executed at the same time. When each web map service is done, an image will be returned to the dispatcher. After all of the web services are done, all images are transparently overlaid together in the dispatcher. Thus, users can browse and analyze the integrated spatial information. Experiments demonstrate that the utilization rate of spatial information resources is significantly raised thought the proposed method of distributed spatial information integration.
High-speed color digital imaging devices are desired for many applications, including civilian and military missions. To capture color images a color sensor has to divide the incidence light into three spectral bands: blue, green and red. Due to the limitation of the spectral bandwidth, the speed of a color digital sensor is significant slower than that of a black and white sensor covering the entire visible bandwidth or the entire bandwidth from visible to near infrared.
This paper introduces the conception of a high-speed color imaging device which can significantly increase the imaging speed without losing image resolution and other image qualities. This device uses the concept of black and white imaging to increase the imaging speed while keeping the image resolution unchanged. It also uses the concept of color imaging to capture color information, but at a significantly lower resolution to allow more incidence light being received by each sensor detector (pixel) to increase the imaging speed. The final color image at the desired resolution is recovered by integrating the spatial information from the black and white image and the color information from the low-resolution color image. An automatic image fusion technique is used to integrate the spatial information and the color information. The quality of the image fusion technique plays a crucial role for quality of the recovered color image. The concept of this high-speed color imaging device has been tested with available low-resolution color images and high-resolution black and white images from the latest commercial satellites, Ikonos and QuickBird. The test results demonstrated that the concept of the high-speed digital color imaging device is promising for producing real high-speed color digital devices for practical applications.
Today's very high spatial resolution satellite sensors, such as QuickBird and IKONOS, pose additional problems to the land cover classification task as a consequence of the data's high spectral variability. To address this challenge, the object-based approach to classification demonstrates considerable promise. However, the success of the object-oriented approach remains highly dependent on the successful segmentation of the image. Image segmentation using the Fractal Net Evolution approach has been very successful by exhibiting visually convincing results at a variety of scales. However, this segmentation approach relies heavily on user experience in combination with a trial and error approach to determine the appropriate parameters to achieve a successful segmentation.
This paper proposes a fuzzy approach to supervised segmentation parameter selection. Fuzzy Logic is a powerful tool given its ability to manage vague input and produce a definite output. This property, combined with its flexible and empirical nature, make this control methodology ideally suited to this task.
This paper will serve to introduce the techniques of image segmentation using Fractal Net Evolution as background for the development of the proposed fuzzy methodology. The proposed system optimizes the selection of parameters by producing the most advantageous segmentation in a very time efficient manner. Results are presented and evaluated in the context of efficiency and visual conformity to the training objects. Testing demonstrates that this approach demonstrates significant promise to improve the object-based classification workflow and provides recommendations for future research.
Radar Shape From Shading (SFS) has been an active research topic on the processing of airborne and spaceborne single polarized SAR images during last two decades. The second generation of spaceborne SAR systems, such as Radarsat-2, will obtain fully polarized information about the ground targets. As the quadrature polarized (quad-pol) SAR images provide useful information on surface roughness and backscatter properties, this research intends to apply the SFS technique to the quad-pol SAR image to obtain topographic information. The technique is tested on the Canadian C/X-SAR (carried on Convair-580) quad-pol data, which is used as simulated sample data of potential Radarsat 2. The results (elevation profiles) are validated with the ground truth elevation profiles, generated from Digital Elevation Model of the area. The geometric accuracies are analyzed.
Multispectral (MS) and hyperspectral (HS) sensors can facilitate target or anomaly detection in clutter since natural clutter and man-made objects diff er in the energy they radiate across the electromagnetic spectrum. Previous research in anomaly detection has formulated two popular algorithms: those based on Gauss-Markov Random Fields (GMRF) and the so-called RX-detector. Performance of these algorithms is dependent on a number of issues including spatial resolution, spectral correlation between the imaging bands, clutter/target model accuracy and the acquired data's signal-to-noise ratio (SNR). This paper provides a comparison study of the anomaly detection performance of the RXdetector and the GMRF-based algorithm using: (1) 4m MS imagery acquired f rom the IKONOS satellite and (2) pansharpened 1m MS imagery created by fusing the 4m MS and the associated 1m panchromatic image sets. The study will be based on the detection performance for stationary and slow moving targets selected f rom imagery acquired during training exercises at Canadian Forces Base (CFB) Petawawa and CFB Wainwright, Canada.
Commercially available very high resolution satellite imagery has reached a sub-meter ground resolution for panchromatic imagery and a few meters of resolution for multispectral imagery (e.g., QuickBird panchromatic 0.6m and multispectral 2.4m). Ground targets such as vehicles can be clearly recognized in the panchromatic imagery, but difficult in the multispectral imagery. For automatic target detection, however, it is desired to have sub-meter multispectral imagery. This paper introduces a new wavelet integrated image fusion approach to produce a sub-meter multispectral image by combining a sub-meter panchromatic image with a several-meter multispectral image. The characteristics of the wavelet transform for spatial detail extraction and advantages of the IHS (Intensity Hue Saturation) fusion techniques are integrated. QuickBird panchromatic and multispectral images are fused. The results are compared with those of other existing image fusion techniques. Visual analyses demonstrate that the new wavelet integrated approach achieves better results for target detection.
High resolution and highly sensitive colour digital sensors are desired for many applications, including military and civilian missions. Due to the limitation of spectral bandwidth, the sensitivity of a digital colour sensor is usually three times lower than that of a digital panchromatic sensor with a spectral bandwidth of the entire visible range or a range from visible to near infrared. This paper introduces a conceptual architecture for producing a triple sensitive colour digital frame sensor. Automatic image fusion techniques are involved to integrate colour and panchromatic images to increase the sensitivity of the colour sensor. Available satellite colour and panchromatic images are tested to prove the concept. The test results demonstrate that the introduced architecture is promising for developing real triple sensitive colour digital sensors.
Rescuing operators of small recreational vessels is a constant resource drain on the limited operating budget of the Canadian Coast Guard. As a result, a new and innovative application of small target surveillance techniques is being developed at the Department of Geodesy and Geomatics Engineering, UNB, Canada. This work is being done in support of the development of a strategic decision making tool based on risk modeling to be used to predict where in Canadian
waters marine incidents are most likely to occur in support of best resource allocation.
Previous research in the use of hyperspectral imaging for search and rescue, resulted in the development of fast, nonparametric
"spatio-spectral" template subpixel object detection algorithm. The results of this work are being adapted and enhanced for use with the new, commercially available spaceborne high-resolution optical imagery. Investigations are being made regarding the utility of the Minkowski distance metrics for use in small target detection within a
multispectral imagery environment. Further, research is being performed on the employment of the Mahalanobis distance metric to enhance the "spatio-spectral" template by exploiting the variance/covariance information surrounding a potential target.
The detection results for the two target vessels were excellent using the Manhattan and Euclidean distance. The best results were had using the Manhattan distance metric with a 5x5 kernel with all 16 yachts detected, no false negatives, and six false positives.
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