Effect of different segmentation methods using optical satellite imagery to estimate fuzzy clustering parameters for Sentinel-1A SAR images

Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm – are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Segmentation and Classification of Multimodal Imagery

Segmentation and classification are two important computer vision tasks that transform input data into a compact representation that allow fast and efficient analysis. Several challenges exist in generating accurate segmentation or classification results. In a video, for example, objects often change the appearance and are partially occluded, making it difficult to delineate the object from its surroundings. This thesis proposes video segmentation and aerial image classification algorithms to address some of the problems and provide accurate results. We developed a gradient driven three-dimensional segmentation technique that partitions a video into spatiotemporal objects. The algorithm utilizes the local gradient computed at each pixel location together with the global boundary map acquired through deep learning methods to generate initial pixel groups by traversing from low to high gradient regions. A local clustering method is then employed to refine these initial pixel groups. The refined sub-volumes in the homogeneous regions of video are selected as initial seeds and iteratively combined with adjacent groups based on intensity similarities. The volume growth is terminated at the color boundaries of the video. The over-segments obtained from the above steps are then merged hierarchically by a multivariate approach yielding a final segmentation map for each frame. In addition, we also implemented a streaming version of the above algorithm that requires a lower computational memory. The results illustrate that our proposed methodology compares favorably well, on a qualitative and quantitative level, in segmentation quality and computational efficiency with the latest state of the art techniques. We also developed a convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification. The CNN features obtained from multiple spectral bands are fused at the initial layers of deep neural networks as opposed to final layers. The early fusion architecture has fewer parameters and thereby reduces the computational time and GPU memory during training and inference. We also introduce a composite architecture that fuses features throughout the network. The methods were validated on four different datasets: ISPRS Potsdam, Vaihingen, IEEE Zeebruges, and Sentinel-1, Sentinel-2 dataset. For the Sentinel-1,-2 datasets, we obtain the ground truth labels for three classes from OpenStreetMap. Results on all the images show early fusion, specifically after layer three of the network, achieves results similar to or better than a decision level fusion mechanism. The performance of the proposed architecture is also on par with the state-of-the-art results