False Color Mapping Research Articles

MASA-SegNet: A Semantic Segmentation Network for PolSAR Images

Semantic segmentation of Polarimetric SAR (PolSAR) images is an important research topic in remote sensing. Many deep neural network-based semantic segmentation methods have been applied to PolSAR image segmentation tasks. However, a lack of effective means to deal with the similarity of object features and speckle noise in PolSAR images exists. Thisstudy aims to improve the discriminative capability of neural networks for various intensities of backscattering coefficients while reducing the effects of noise in PolSAR semantic segmentation tasks. Firstly, we propose pre-processing methods for PolSAR image data, which consist of the fusion of multi-source data and false color mapping. Then, we propose a Multi-axis Sequence Attention Segmentation Network (MASA-SegNet) for semantic segmentation of PolSAR data, which is an encoder–decoder framework. Specifically, within the encoder, a feature extractor is designed and implemented by stacking Multi-axis Sequence Attention blocks to efficiently extract PolSAR features at multiple scales while mitigating inter-class similarities and intra-class differences from speckle noise. Moreover, the process of serialized residual connection design enables the propagation of spatial information throughout the network, thereby improving the overall spatial awareness of MASA-SegNet. Within the decoder, it is used to accomplish the semantic segmentation task. The superiority of this algorithm for semantic segmentation will be explored through feature visualization. The experiments show that our proposed spatial sequence attention mechanism can effectively extract features and reduce noise interference and is thus able to obtain the best results on two large-scale public datasets (the AIR-POlSAR-Seg and FUSAR-Map datasets).

Adaptive optimization of reference intensity for optical coherence imaging using galvanometric mirror tilting method

Integration time and reference intensity are important factors for achieving high signal-to-noise ratio (SNR) and sensitivity in optical coherence tomography (OCT). In this context, we present an adaptive optimization method of reference intensity for OCT setup. The reference intensity is automatically controlled by tilting a beam position using a Galvanometric scanning mirror system. Before sample scanning, the OCT system acquires two dimensional intensity map with normalized intensity and variables in color spaces using false-color mapping. Then, the system increases or decreases reference intensity following the map data for optimization with a given algorithm. In our experiments, the proposed method successfully corrected the reference intensity with maintaining spectral shape, enabled to change integration time without manual calibration of the reference intensity, and prevented image degradation due to over-saturation and insufficient reference intensity. Also, SNR and sensitivity could be improved by increasing integration time with automatic adjustment of the reference intensity. We believe that our findings can significantly aid in the optimization of SNR and sensitivity for optical coherence tomography systems.

Spectropolarimetric imaging for pathological analysis of skin

Pathological changes of skin will change the tissue's birefringence and structure, which can be measured by the polarization and spectral changes of the tissue's scattering light. Light-scattering spectropolarimetry is an effective tool to measure these features for quantitative pathology analysis. An epithelial tissue imaging spectropolarimeter is proposed to acquire the spectral, polarimetric, and spatial characteristic changes of the tissue, and then a spectropolarimetric correction method is proposed to compute the tissue's polarimetric spectrum, which can be used for pathological analysis of tissue. Finally, to aid doctors for more accurate clinical diagnosis, a false color mapping based spectropolarimetric image fusion method is proposed to enhance the visual differences between normal skin and pathological skin. Experimental results demonstrate the potential of the proposed techniques for pathological diagnosis and treatment evaluation of skin.

Monitoring PDT-induced damage using spectrally resolved reflectance imaging of tissue oxygenation

Photodynamic therapy (PDT) with chlorin e 6 (Chl) was monitored in vivo using vital microscopy and Fourier transform spectral imaging (FT-SI). Mammary C26 colon carcinoma, implanted intradermally in a mouse, was irradiated at 650 nm with various radiant exposures, 3 h after administration of 5 mg/kg Chl. The photodynamic response (PDR) in the skin flap with tumor was expressed as microcirculation disturbances (thrombi formation, multiple embolizations, arteriolar occlusion and venous stasis) and, dependent on the radiant exposure, was transient or permanent. These biological manifestations were accompanied by a change in hemoglobin (Hb)/oxyhemoglobin (HbO 2) absorption spectra obtained in vivo by FT-SI. False-color mapping of hemoglobin oxygen saturation (OS) visualized the alteration of tissue oxygenation. The results demonstrate, for the first time, that FT-SI can serve as a sensitive non-invasive tool for OS monitoring of PDT effects.

Spectroscopic optical coherence tomography

Spectroscopic optical coherence tomography (OCT), an extension of conventional OCT, is demonstrated for performing cross-sectional tomographic and spectroscopic imaging. Information on the spectral content of backscattered light is obtained by detection and processing of the interferometric OCT signal. This method allows the spectrum of backscattered light to be measured over the entire available optical bandwidth simultaneously in a single measurement. Specific spectral features can be extracted by use of digital signal processing without changing the measurement apparatus. An ultrabroadband femtosecond Ti:Al(2)O(3) laser was used to achieve spectroscopic imaging over the wavelength range from 650 to 1000 nm in a simple model as well as in vivo in the Xenopus laevis (African frog) tadpole. Multidimensional spectroscopic data are displayed by use of a novel hue-saturation false-color mapping.