Davut Çeşmeci

Spatial Resolution Enhancement of Hyperspectral Images Using Unmixing and Binary Particle Swarm Optimization

Hyperspectral imaging provides high spectral resolution and thereby improved classification, detection, and recognition capabilities with respect to standard imaging systems. However, hyperspectral images generally have low spatial resolution, varying from a few to tens of meters, resulting from technical limitations such as platform data storing capacity and satellite-to-ground transmission bandwidth. Spectral unmixing provides information on pixels in terms of abundances of pure spectral signatures, without providing spatial distribution at subpixel level. Multisensor image fusion approaches can provide such information but require an additional image with higher spatial resolution that is acquired in similar conditions with the hyperspectral image. In this letter, a novel spatial resolution enhancement method using fully constrained least squares (FCLS) spectral unmixing and spatial regularization based on modified binary particle swarm optimization is proposed to achieve spatial resolution enhancement in hyperspectral images, without using an additional image with higher spatial resolution. The proposed method has a highly parallel nature with respect to its counterparts in the literature and is fit to be adapted to field-programmable gate array architecture.

Image processing-aided working posture analysis

OWAS method is adapted to an integrated software as a prototype.It operates completely computer-aided with the help of image processing techniques.That models performance is high while robust recording conditions can be settled.Necessity of expert analyst is eliminated.The model will support the common use of OWAS in industry. Musculoskeletal Disorders (MSDs) rank among the commonest health problems both in the frequency of concurrency and in the money spent on these disorders, which mainly stem from poor working posture it also negatively affects employees in terms of job productivity, life quality, and both physical and social activities. Analyzing and improving working postures with scientific methods provides significant contributions in the field of controlling job performance and decreasing MSDs. OWAS (Ovako Working Posture Analyzing System) is one of the methods for analyzing working postures and can be applied to very diverse areas successfully. In this study, a prototype of integrated software, which is based on image processing techniques, was developed (I-OWAS), and the performance of the model was presented. I-OWAS begins with separating the video film into frames, producing OWAS codes belonging to working posture in each frame, and then classifying the images according to risk categories. Despite OWAS being a successful method for analyzing working postures, it requires an expert analysis. Also the manual analyzing process is so laborious and time consuming. I-OWAS provide the computer support for the manual coding stage and eliminates the need for an expert analyst; hence, the method can be widely used in industry.

Endmember Extraction Guided by Anomalies and Homogeneous Regions for Hyperspectral Images

Endmember extraction is the process of selecting pure spectral signatures of materials from hyperspectral data. Most of the endmember extraction methods in the literature use only the spectral information, and disregard the spatial composition of the image. Spatial-spectral preprocessing methods, motivated by the assumption that endmembers are more likely to be located in homogenous regions, can increase the performance of endmember extraction by directing the extraction process to homogenous regions. However, such an approach generally results in a failure of extracting anomalous or scarce endmembers, which can be important in practical applications, e.g., to extract endmembers of materials such as landmines, rare minerals, or stressed crops. Although anomaly detection can be applied in parallel to endmember extraction, the process of endmember extraction and unmixing provides a summary of the data, which is important for concepts such as data scanning and compression, and disregarding anomalous endmembers in such a summary or compression of big data may result in undesired consequences for many application fields. In this paper, an approach that guides the endmember extraction process to spatially homogenous regions instead of transition areas, while also extracting anomalous pixel vectors as endmembers, is proposed. The proposed approach can be used with any spectral-based endmember extraction method. The experimental results validate the approach for both synthetic and real hyperspectral images.

Integrating anomaly detection to spatial preprocessing for endmember extraction of hyperspectral images

Spectral unmixing is the process of identifying pure spectral signatures, called endmembers, from a hyperspectral data, and then expressing each pixel vector in terms of the fractional abundances of these endmembers. Most of the endmember extraction methods in the literature use only the spectral information, whereas the spatial composition of the data is disregarded. Spatial preprocessing methods, that are motivated by the assumption that endmembers are more likely to be located in homogeneous regions instead of transition areas, can alleviate this drawback and hence increase the performance. However, such a preprocessing approach generally results in a failure of extracting anomalous endmembers which can be of importance for many applications. In this paper, a preprocessing approach that guides the endmember extraction process to homogenous regions while retaining the anomaly points, by combining spatial preprocessing with anomaly detection, is proposed.

Local spectral gradient based gas detection in LWIR hyperspectral images

In this work, the use of local gradient features of spectral signatures with spectral angle mapper for target detection in long wave infrared hyperspectral images is proposed. The proposed method is tested for gas detection and experimental results show that utilizing local gradient features improve the performance.

Hyperspectral change detection with stereo disparity information enhancement

A hyperspectral change detection method with stereo depth information enhancement is proposed in this paper. The method operates on the hyperspectral data acquired by the ground-based hyperspectral stereo imaging system. The imaging system combines the properties of panoramic and stereo imaging with the high spectral resolution of hyperspectral cameras, and is of use especially for surveillance applications. Stereo and spectral information provided by the system are fused in the proposed method for enhanced change detection. Experimental results are evaluated on two hyperspectral datasets acquired by the system. Preliminary results show the improved performance of the system and the proposed method.

Hyperspectral change detection by multi-band Census Transform

Hyperspectral imaging provides increased capability for many image processing tasks with respect to standard imaging systems. One of such tasks in hyperspectral image processing is change detection, which aims to detect the differences occurring between images acquired from the same scene at different times. In this paper, a panoramic hyperspectral imaging system is used to capture multitemporal hyperspectral data, and novel multi-band Census Transform (MCT) is proposed for change detection on these data. Experimental results validate the performance of the proposed method for the utilized acquisition system.

Segmentation of hyperspectral images using phase correlation based on adaptive thresholding

This letter presents hyperspectral image segmentation based on the phase-correlation measure and updating the segments using a post processing operation based on adaptive thresholding. Spectral signature of each pixel is subsampled to gain robustness against noise and spatial variability, and phase correlation is performed to measure spectral similarity. Similar and dissimilar pixels are decided according to the peak value of the phase correlation result to determine pixels that fall into the same segments. An adaptive threshold value that is determined for each segment considering in-segment similarity distribution is used to update the segment. Segmentation accuracy is increased compared to phase correlation based segmentation.

An automated fine registration of multisensor remote sensing imagery

In this study we propose an automated fine registration of EO-1 Hyperion and IKONOS imagery. An intensity based registration that is area-based and pixelwise is performed to register given images of divergent spatial and spectral resolution. Two similarity measures that are commonplace in image registration; NCC and NMI, and an operation that is particular to image restoration; CTO is adopted as an error measure as a novelty in image registration. We are convinced with the performance and efficiency of CTO compared to other two common methods of intensity-based registration.

Fusion of hyperspectral and multispectral images using general and local spectral features

In this paper, the use of local and general features of spectral signatures separately for the fusion of hyperspectral and multispectral images is proposed. Nonnegative Matrix Factorization algorithm is used for fusion process. High resolution hyperspectral image is obtained by merging fusion images that produced for general and local spectral features. The proposed method is tested on sentetic and real hyperspectral images. Experimental studies show that carrying out fusion process individually for general and local features and then merging the fusion results improves the fusion performance.