Semantic segmentation has crucial importance in various domains due to its ability to recognize and categorize objects within an image at a pixel level. This task enables a wide range of applications, such as autonomous vehicles, environmental monitoring, and remote sensing (RS). In RS, semantic segmentation plays a crucial role, acting as the basis for applications including land cover classification. Following the success of deep learning (DL) methods in computer vision, our paper addresses the intersection between DL and RS imagery. We focus on improving the efficiency of some baseline and backbone models to ensure their adaptability to the challenges posed by RS imagery. Therefore, we evaluate state-of-the-art models on two datasets and investigate their ability to accurately segment objects in RS imagery. Our research aims to open the way for more accurate and reliable semantic segmentation methods in geospatial analysis.
While RGB imaging is reaching its limits, Hyperspectral Imaging (HSI) is being widely used especially for medical applications. This study points out the ability of HSI technique to help in planning the surgical procedure in orthopedic surgery by automatically identifying anatomical structures and surgical instruments thanks to their spectral signatures. Four segmentation methods have been explored: (i) average spectra method that uses the Euclidean distance between the spectrum of each pixel and the average spectrum of each specific structure, (ii) segmentation using kmeans, (iii) segmentation based on indices in which we identify reflectances ratios at specific wavelengths that allow materials to be correctly classified, (iv) and finally a pixel-based classification method based on neural networks. Experiments on anatomical objects whose physical characteristics are known to have been carried out. Selecting specific wavelengths to reduce the cost of the final device was also discussed.
Polarization-resolved extension of Second Harmonic Generation microscopy (PSHG) exhibits proven efficiency in cancer diagnosis. Contrary to the case of white light microscopy, PSHG can reveal small structural collagen changes, during tumorigenesis, for a broad range of organs such as breast, thyroid, lung, pancreas, and ovary. However, despite its effectiveness for cancer diagnosis, PSHG is not yet fully exploited. One way of improvement consists in taking better advantage of polarization-resolved measurements which are performed by acquiring multiple images (usually between three to 20) of the same sample under different input beam polarization conditions. Each image of the resulting stacked raw images set can contain relevant information not found in the other images of the set. In the literature, information extraction from stacked raw images is performed using methods such as averaging of all images, collagen structural parameters modeling or PSHG polarimetric parameters extraction. If the two latter methods provide a richer information than the first one, they may, however, suffer from a loss of information from the stacked raw images. To examine this potential loss of information, AI methods can be used for extracting information from the stacked raw images. Using recently available images of the public SHG-TIFF database, dealing with breast and thyroid PSHG measurements of both normal and tumor tissues, we test available AI methods for information extraction and benchmark these methods to the state-of-the-art, in terms of automatic cancer diagnosis efficiency.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.