Alp Ertürk

Two-bit transform for binary block motion estimation

One-bit transforms (1BTs) have been proposed for low-complexity block-based motion estimation by reducing the representation order to a single bit, and employing binary matching criteria. However, as a single bit is used in the representation of image frames, bad motion vectors are likely to be resolved in 1BT-based motion estimation algorithms particularly for small block sizes. It is proposed in this paper to utilize a two-bit transform (2BT) for block-based motion estimation. Image frames are converted into two-bit representations by a simple block-by-block two bit transform based on multithresholding with mean and linearly approximated standard deviation values. In order to avoid blocking effects at block boundaries during the block-by-block transformation while enabling the two-bit representation to be constructed according to local detail, threshold values are computed within a larger window surrounding the transforming block. The 2BT makes use of lower bit-depth and binary matching criteria properties of 1BTs to achieve low-complexity block motion estimation. The 2BT improves motion estimation accuracy and seriously reduces the amount of bad motion vectors compared to 1BTs, particularly for small block sizes. It is shown that the proposed 2BT-based motion estimation technique improves motion estimation accuracy in terms of peak signal-to-noise ratio of reconstructed frames and also results in visually more accurate frames subsequent to motion compensation compared to the 1BT-based motion estimation approach.One-bit transforms (1BTs) have been proposed for low-complexity block-based motion estimation by reducing the representation order to a single bit, and employing binary matching criteria. However, as a single bit is used in the representation of image frames, bad motion vectors are likely to be resolved in 1BT-based motion estimation algorithms particularly for small block sizes. It is proposed in this paper to utilize a two-bit transform (2BT) for block-based motion estimation. Image frames are converted into two-bit representations by a simple block-by-block two bit transform based on multithresholding with mean and linearly approximated standard deviation values. In order to avoid blocking effects at block boundaries during the block-by-block transformation while enabling the two-bit representation to be constructed according to local detail, threshold values are computed within a larger window surrounding the transforming block. The 2BT makes use of lower bit-depth and binary matching criteria properties of 1BTs to achieve low-complexity block motion estimation. The 2BT improves motion estimation accuracy and seriously reduces the amount of bad motion vectors compared to 1BTs, particularly for small block sizes. It is shown that the proposed 2BT-based motion estimation technique improves motion estimation accuracy in terms of peak signal-to-noise ratio of reconstructed frames and also results in visually more accurate frames subsequent to motion compensation compared to the 1BT-based motion estimation approach.

Unsupervised Segmentation of Hyperspectral Images Using Modified Phase Correlation

This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain robustness against noise and spatial variability, and phase correlation is applied to determine 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. The approach can be regarded as a region-growing technique. The total number of segments is determined automatically according to the similarity thresholdThis letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain robustness against noise and spatial variability, and phase correlation is applied to determine 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. The approach can be regarded as a region-growing technique. The total number of segments is determined automatically according to the similarity threshold

Hyperspectral Image Classification Using Empirical Mode Decomposition With Spectral Gradient Enhancement

This paper proposes to use empirical mode decomposition (EMD) with spectral gradient enhancement to increase the classification accuracy of hyperspectral images with support vector machine (SVM) classification. Recently, it has been shown that higher hyperspectral image classification accuracy can be achieved by using 2-D EMD that is applied to each hyperspectral band separately to obtain the intrinsic mode functions (IMFs) of each band, while the sum of the IMFs are used as feature data in the SVM classification process. In the previous approach, IMFs have been summed directly, i.e., with equal weights. It is shown in this paper, that it is possible to significantly increase the classification accuracy by using appropriate weights for the IMFs in the summation process. In the proposed approach, the weights of the IMFs are obtained so as to optimize the total absolute spectral gradient, and a genetic algorithm-based optimization strategy has been adopted to obtain the weights automatically in this way. While the 2-D EMD basically provides spatial processing, the proposed method further incorporates spectral enhancement into the process. It is shown that a significant increase in hyperspectral image classification accuracy can be achieved using the proposed approach.

Sparse Unmixing-Based Change Detection for Multitemporal Hyperspectral Images

In recent years, the increased availability of spectral libraries has resulted in a growing interest in sparse unmixing, which aims to find an optimal subset of library signatures to represent the pixels of remotely sensed hyperspectral datasets as linear combinations of these signatures. Sparse unmixing sidesteps two important drawbacks of the regular spectral unmixing process, namely the difficulty of estimating the number of endmembers, and the process of extracting the endmembers itself, the result of which will vary according to the utilized extraction method. In this work, sparse unmixing is exploited for the first time in the context of multitemporal hyperspectral data analysis and change detection. Change detection by sparse unmixing based on spectral libraries has the important advantage of providing not only pixel-level but also subpixel-level change information for the hyperspectral data. The changes that occur in multitemporal datasets due to time or as a result of a significant event are revealed, at subpixel-level, as the abundances of underlying endmembers within a pixel, or as variations in the distribution of these endmembers throughout the scene. The proposed approach is validated by experimental studies on both carefully prepared synthetic datasets and real datasets, using different spectral libraries.

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.

Enhanced Unmixing-Based Hyperspectral Image Denoising Using Spatial Preprocessing

Unmixing provides a summary of hyperspectral data and is useful for many image processing tasks. Recently, spectral unmixing has also been introduced to hyperspectral image denoising literature. However, so far, only the spectral information has been utilized for unmixing-based denoising. While most of the endmember extraction methods in the literature rely solely on spectral information, it has been shown that spatial-spectral preprocessing (SSPP) methods can enhance endmember extraction performance by utilizing the assumption that endmembers are more likely to be located in homogenous regions. This letter proposes the use of SSPP prior to spectral unmixing, to guide the endmember extraction process to spatially homogenous regions. The enhanced endmember extraction performance in turn leads to enhanced denoising performance. In addition, the proposed approach also goes one step further and retains the anomalous/scarce endmembers, which may include important endmembers, such as rare minerals, stressed crops, or military targets, and which may be lost due to the included spatial preprocessing (SPP) steps. Discarding such anomalous endmembers in a summary or compression of big data may result in undesired consequences. In short, the proposed approach provides enhanced unmixing-based denoising performance, while also retaining the anomalous endmembers.

Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection

The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of only providing the location of the changes. Recently, sparse unmixing has also been introduced to hyperspectral change detection, resulting in a method that circumvents the drawbacks of regular spectral unmixing approaches. Sparse unmixing-based change detection reveals the changes that occur in a multitemporal series, at subpixel level, and in terms of the library spectra and their sparse abundances, and provides enhanced change detection performance, especially when subpixel-level changes have occurred. However, sparse unmixing is generally an ill-conditioned and time-consuming process, especially as the size of the utilized spectral library increases. In this paper, dictionary pruning is exploited for the first time for hyperspectral change detection using sparse unmixing, in order to alleviate the ill-conditioning of the problem and achieve decreased computation times and enhanced change detection performance. Experimental results on both realistic synthetic and real datasets are used to validate the proposed approach.

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.

Plastic waste sorting using infrared hyperspectral imaging system

The inherent chemical properties of materials can be brought into perspective using the large amount of spectral information provided by hyperspectral imaging systems. Therefore, the utilization of hyperspectral imaging in industrial applications is gradually increasing. One of the industrial sectors that can benefit from the advantages of hyperspectral imaging is recycling. Plastics which have different chemical properties (PP, PE, PVC, PET and PS) need sorting for plastic waste recycling. In this study, different types of plastics in hyperspectral images acquired using a shortwave infrared (SWIR) hyperspectral imaging system are successfully sorted.