SUPERPIXEL BASED FACTOR ANALYSIS AND TARGET TRANSFORMATION METHOD FOR MARTIAN MINERALS DETECTION

This paper investigates the application of the Simple Linear Iterative Clustering (SLIC) algorithm for complex object image segmentation, on the example of images of human body injuries. The study solves the problem of the lack of quantitative evidence regarding SLIC's algorithm performance in high-precision area and boundary assessment of the lesion on a digital image of a human body with a wound injury on it. A comprehensive methodology is developed to evaluate SLIC's algorithm efficacy across various complex images and image resolutions. The research utilizes a combined dataset of 3696 wound images from the Foot Ulcer Segmentation Challenge (FUSeg) and WoundSeg datasets. Bayesian optimization is utilized to fine-tune SLIC algorithm hyperparameters, focusing on the number of segments and compactness. Results indicate that SLIC algorithm demonstrates consistent performance across different implementations, achieving Dice scores around 0.84 and Soft Boundary F1 scores around 0.55. The study reveals that the optimal number of segments for SLIC algorithm can be defined relative to the spatial dimensions of the input image, with maximal image dimension *2 being the most effective value. A thorough analysis of various segmentation metrics is conducted, including IoU, Dice Score, and Boundary F1 Score. The research introduces and employs the Soft Boundary F1 Score – modification of Boundary F1 Score, a novel metric designed to provide a more nuanced evaluation of boundary detection accuracy while offering a smoother optimization landscape. This metric proves particularly valuable in assessing the performance of SLIC algorithm in image with complex objects on them segmentation tasks. Importantly, this research presents an idealized SLIC-based segmentation approach, where superpixels are optimally combined using ground-truth masks to establish an upper bound of performance. This idealized SLIC algorithm segmentation is compared with a pre-trained on the FUSeg dataset UNet model, showcasing superior generalization capability across diverse wound types. On the WoundSeg dataset, the idealized SLIC algorithm approach achieved a Dice score of 0.84, significantly outperforming the UNet model (0.12 Dice score). As a result, this study provides valuable insights for improving complex objects segmentation methods and highlights the need for further research on developing effective methods for superpixel classification in real-world scenarios. The findings also highlight the potential of SLIC-based approaches in addressing the challenges of diverse data types and limited training data.

Simple Linear Iterative Clustering (SLIC) algorithm is increasingly applied to different kinds of image processing because of its excellent perceptually meaningful characteristics. In order to better meet the needs of medical image processing and provide technical reference for SLIC on the application of medical image segmentation, two indicators of boundary accuracy and superpixel uniformity are introduced with other indicators to systematically analyze the performance of SLIC algorithm, compared with Normalized cuts and Turbopixels algorithm. The extensive experimental results show that SLIC is faster and less sensitive to the image type and the setting superpixel number than other similar algorithms such as Turbopixels and Normalized cuts algorithms. And it also has a great benefit to the boundary recall, the robustness of fuzzy boundary, the setting superpixel size and the segmentation performance on medical image segmentation.

Analysis of phyllosilicate deposits in the Nili Fossae region of Mars: Comparison of TES and OMEGA data

For better analyzing and adopting superpixel methods in image segmentation, an improved Simple Linear Iterative Clustering (SLIC) algorithm is put forward, which sustains that homogeneous regions show high consistence during clustering. Firstly, a primary iteration of SLIC approach is employed to generate uniform superpixels. Then an update strategy is introduced to determine whether to recluster the candidate region. In homogeneous regions, a cluster center often shows relatively small deviation of last position in spatial-color feature space when local iteration occurs, which can be regarded as redundant computation and be reduced. Experimental results show that the strategy brings a remarkable increased efficiency for traditional SLIC without significant affection on segmentation results.

Compact Reconnaissance Imaging Spectrometer for Mars(CRISM)은 489개의 밴드를 가지는 화성정찰궤도선의 초분광 카메라로써 이를 이용한 화성 지표의 광물 분포에 대한 많은 연구가 진행되어 왔다. 본 연구에서는 USGS의 스펙트럼 라이브러리를 기반으로 화성 Gusev Crater의 Spirit(Mars Exploration Rover A) 로버 착륙지에 대한 CRISM 영상에 Matched Filter와 Adaptive Cosine Estimator(ACE) 표적 탐지 알고리즘을 적용하여 광물 분포를 확인하고자 하였다. 연구 결과 감람석, 휘석, 자철석 등의 광물들이 Gusev 크레이터의 Columbia Hills에서 탐지되어 Spirit 로버의 지상 탐사 결과와 일치하고 있음을 확인하였다. 본 연구는 그간 CRISM의 광물 분포 연구가 일부 몇 개 밴드의 반사도만을 통해 계산된 광물 지수에 의존하던 것에서 관측 파장 대역 전체를 활용하는 초분광 표적 탐지 알고리즘을 이용한 새로운 적용방법을 제시한 것에 의의가 있다고 할 수 있다. Compact Reconnaissance Imaging Spectrometer for Mars(CRISM) is 489-band hyperspectral camera of Mars Reconnaissance Orbiter(MRO) that provided data used on many mineral researches over Martian surface. For the detection of minerals in planet, mineral index using a few spectral bands have been used. In this study, we applied Matched Filter and Adaptive Cosine Estimator(ACE) target detection algorithm on CRISM data over Gusev Crater: landing site of Spirit(Mars Exploration Rover A) to investigate its mineral distribution. As a result, olivine, pyroxene, magnetite, etc. is detected at Gusev Crater's Columbia Hills. These results are corresponding to the Spirit rover's field survey result. It is expected that hyperspectral target detection algorithms can be used as effective and easy to use method for the detection and mapping of surface minerals in planet.

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

Extensive research over the past two decades has shown that early Mars likely had a warmer, wetter climate with widespread water activity. Ferromagnesian (Fe,Mg‐rich) clay deposits are compelling markers of these ancient environments, helping reconstruct Mars' hydrologic evolution, assess past habitability, and guide future exploration. This study analyzes hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard NASA's Mars Reconnaissance Orbiter , focusing on regions along the Martian crustal dichotomy—where clay deposits occur at the boundary between the ancient southern highlands and the younger northern lowlands. We systematically surveyed ∼1500 CRISM targeted observations (1–2.6 μm) to identify ferromagnesian clays, distinguish them from other hydrated minerals, and characterize compositional differences between Fe‐ and Mg‐rich species using diagnostic absorptions around 1.4, 2.3, and 2.4 μm. Results reveal spatial variations in clay mineralogy: Fe‐rich nontronites are prevalent around Mawrth Vallis, while Mg‐rich saponites are more locally distributed in Nili Fossae and Libya Montes. Oxia Planum—the Rosalind Franklin rover landing site—exhibits more compositionally intermediate clays such as vermiculites and ferrosaponites. These differences may reflect variations in the iron and magnesium abundance or in the iron oxidation state. Moreover, a recurring absorption near 2.5 μm suggests co‐occurring carbonates like magnesite and siderite, increasing the potential for biosignature preservation. These findings refine our understanding of Mars' aqueous history and offer an important mineralogical context for future rover and sample return missions. They also emphasize the need for a next‐generation orbital imaging spectrometer to succeed CRISM and extend its legacy.

In view interpolation, information missing often exists in initial depth map, moreover, disocclusion regions usually occur along the foreground object boundaries after 3D warping. Generally, initial depth map and warped depth map have a strong influence on the performance of view interpolation. However, most of existing view interpolation algorithms only emphasize hole filling of the warped color image. In this paper, a superpixel-based method is proposed for initial depth map enhancement and warped depth map hole filling. Firstly, the color image is segmented using simple linear iterative clustering (SLIC) algorithm, and after that, the associated depth map is segmented with the same label. Then, the depthmissing pixels are recovered by considering color and depth superpixel information jointly. Additionally, holes of the disocclusion regions in the warped depth map can also be filled efficiently via superpixel-based segmentation. Experimental results show that with the proposed method the quality of the interpolated view has been improved significantly in terms of both subjective and objective evaluations.

Alzheimer's Disease (AD) poses many challenges to healthcare systems for elderly people in the world, for which an accurate diagnosis of AD is therefore a crucial task. Positron Emission Tomography (PET) images and machine learning can be used to support this purpose. Brain atlases are common tools used to extract features from PET images, and choosing a proper brain map is extremely important. In this work, we propose a novel method to learn the very first PET-driven brain mapping based on Support Vector Machine (SVM) coefficients and Simple Linear Iterative Clustering (SLIC) algorithm, which is addressed as C-Atlas. When compared with predefined atlases on classification task, C-atlas experimentally proved to be more adapted for AD diagnosis both in terms of feature extraction and selection. The results suggest that it is possible to build an adapted brain mapping for a specific disease, and that the proposed method can also be used as an effective mean to support the analysis and comparison of brain diseases at multiple scales.

Road detection aims at detecting the (drivable) road surface ahead vehicle and plays a crucial role in driver assistance system. To improve the accuracy and robustness of road detection approaches in complex environments, a new road detection method based on CNN (convolutional neural network) and MRF (markov random field) is proposed. The original road image is segmented into super-pixels of uniform size using simple linear iterative clustering (SLIC) algorithm. On this basis, we train the CNN which can automatically learn the features that are most beneficial to classification. Then, the trained CNN is applied to classify road region and non-road region. Finally, based on the relationship between the super-pixels neighborhood, we utilize MRF to optimize the classification results of CNN. Quantitative and qualitative experiments on the publicly datasets demonstrate that the proposed method is robust in complex environments. Furthermore, compared with state-of-the-art algorithms, the approach provides the better performance.

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.

Video is a rich information source containing both audio and visual information along with motion information embedded in it. Applications such as e-learning, live TV, video on demand, traffic monitoring, etc. need an efficient video retrieval strategy. Content-based video retrieval and superpixel segmentation are two diverse application areas of computer vision. In this work, we are presenting an algorithm for content-based video retrieval with help of Integration of Curvelet transform and Simple Linear Iterative Clustering (ICTSLIC) algorithm. Proposed algorithm consists of two steps: off line processing and online processing. In offline processing, keyframes of the database videos are extracted by employing features: Pearson Correlation Coefficient (PCC) and color moments (CM) and on the extracted keyframes superpixel generation algorithm ICTSLIC is applied. The superpixels generated by applying ICTSLIC on keyframes are used to represent database videos. On other side, in online processing, ICTSLIC superpixel segmentation is applied on query frame and the superpixels generated by segmentation are used to represent query frame. Then videos similar to query frame are retrieved through matching done by calculation of Euclidean distance between superpixels of query frame and database keyframes. Results of the proposed method are irrespective of query frame features such as camera motion, object’s pose, orientation and motion due to the incorporation of ICTSLIC superpixels as base feature for matching and retrieval purpose. The proposed method is tested on the dataset comprising of different categories of video clips such as animations, serials, personal interviews, news, movies and songs which is publicly available. For evaluation, the proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Experiments were conducted on the developed dataset and the performance is assessed with different parameters Precision, Recall, Jaccard Index, Accuracy and Specificity. The experimental results shown that the proposed method is performing better than the other state-of-art methods.

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for pre-segmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.

Over recent decades, remote sensing has emerged as an effective tool for improving agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi-resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchical segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra-variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a single image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.

To understand the aqueous history of Mars, it is critical to constrain the alteration mineralogy of the Martian surface. Previously published analyses of thermal infrared (λ = 6–25 μm) remote sensing data of Mars suggest that dark regions have ∼15–20% clay minerals. However, near‐infrared (λ = 1–3 μm) spectral results generally do not identify widespread clay minerals. Thermal infrared detections of clays on Mars are difficult to interpret owing in part to the relative paucity of published spectral analyses of clay minerals and clay‐bearing materials using similar spectra (thermal infrared emission spectra). In this study, we present an analysis of the thermal emission spectral features (λ = ∼6–25 μm or 400–1650 cm−1) of a suite of clay mineral reference materials and clay‐bearing rocks, linking their spectral features to the crystal chemical properties of the clays. On the basis of this context provided by the emission spectral analysis of clay minerals and clay‐bearing rocks, we reconsider the evidence for clay minerals on Mars from Thermal Emission Spectrometer (TES) results. We propose that global‐scale clay abundances determined from TES probably represent a geologically significant surface component, though they may actually correspond to poorly crystalline aluminosilicates with similar Si/O ratios to clay minerals (0.3–0.4), rather than well‐crystalline clays. If clay minerals or clay‐like materials on Mars are poorly crystalline and/or dessicated, they may be detectable in the thermal infrared, but not easily detected with near‐infrared data sets.