Kuniaki Uto

Cancer detection using infrared hyperspectral imaging

During the last few decades, many studies have been performed on the early detection of cancer using noninvasive or minimally invasive techniques in lieu of traditional excisional biopsy. Early detection can make an immense difference because cancer treatment is often simpler and more effective when diagnosed at an early stage. Cancer detecting methods may help physicians to diagnose cancer, to dissect the malignant region with a safe margin, and to evaluate the tumor bed after resection. In this paper, the advanced hyperspectral imaging system has been assessed using infrared wavelengths region for tumor detection. We were able to identify an appropriate wavelength region for cancer detection, spatially resolved images, and highlight the differences in reflectance properties of cancerous versus non‐cancerous tissues. The capability of this instrument was demonstrated by observing gastric tumors in 10 human subjects. The spectral signatures were extracted and evaluated in cancerous and non‐cancerous tissues. Processing means with the standard deviation of the spectral diagram, support vector machine, and first derivatives and integral of in spectral diagram were proposed to enhance and detect the cancerous regions. The first derivatives in spectral region between 1226–1251 nm and 1288–1370 nm were proposed as criteria that successfully distinguish between non‐cancerous and cancerous tissue. The results of this study will lead to advances in the optical diagnosis of cancer. (Cancer Sci 2011; 102: 852–857)

Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System

A low-cost, small, lightweight hyperspectral sensor system that can be loaded onto small unmanned autonomous vehicle (UAV) platforms has been developed for the acquisition of aerial hyperspectral data. Safe and easy observation is possible under unstable illumination conditions by using lightweight and autonomous cruising. The hyperspectral sensor system, equipped with a 256-band hyperspectral sensor covering a spectral range from 340-763 nm, a GPS and a data logger, is 400 g in total weight. The acquisition period for each sampling, 768 bytes, is 100 ms. The aerial hyperspectral data of rice paddies are collected under cloudy weather. The flight altitude from the ground is 10 m, and the cruising speed is 2 m/s. The high-accuracy estimation of the chlorophyll densities is confirmed, even under unstable illumination conditions, by frequent monitoring of the illumination level and the chlorophyll indices, based on the red-edge (RE) and near infrared (NIR) spectral ranges.

Automatic detection of damaged area of Iran earthquake by high-resolution satellite imagery

The huge earthquake of magnitude 6.3 had occurred at Bam city of southeastern Iran on December 26, 2003. The author applied nonlinear mapping method for detection of collapsed building area due to Iran earthquake with high-resolution QuickBird satellite images. As a result, ROC evaluation based on the result of photo interpretation by operator showed that the detection ratio of the proposed method is 75-90% of changed area with 20% of false alarm.

Multispectral image enhancement for effective visualization

Color enhancement of multispectral images is useful to visualize the images spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.

ICA-aided mixed-pixel analysis of hyperspectral data in agricultural land

This letter proposes an independent component analysis (ICA)-aided mixed-pixel analysis of periodically distributed hyperspectral data in agricultural land. This method simultaneously estimates the pure spectra and coverage of endmembers, such as crop and soil, from mixed-pixel data which is inevitably included in images observed from high-altitude sensors. The method is effective for agricultural management because the change of observed mixed-pixel data is distinguished into a qualitative spectral one, due to chlorophyll quantity or crop variety, and the quantitative coverage due to growth stages. This method introduces a priori knowledge which is independent of the type of crop and effective in deriving a scaling factor for the independent component (IC), estimated from the ICA process. The fundamental investigation, using hyperspectral data obtained from a crane and an aircraft, shows the applicability of the method.

Development of a Low-altitude Hyperspectral Imaging System for Measuring Ground Truth in Agricultural Fields

In the hyperspectral analyses over large agricultural fields, it is necessary to utilize the data acquired by high-altitude platform such as satellites or airplanes. However, in the preprocessing stage of the analyses, it is important to provide with high signal-to-noise ratio (SNR) data acquired on the ground surface, to indicate the ground-truth. In order to obtain the ground-truth hyperspectral data constantly, a low-altitude data acquisition system using a cargo crane with an artificial illumination source is developed. The system can collect data in actual agricultural fields without being seriously affected by the weather condition. Various types of validation data are collected by the system and compared in order to evaluate the performance of the system.

Salt-damaged paddy fields analyses using high-spatial-resolution hyperspectral imaging system

In agricultural fields, the damage caused by salinized winds is crucial for crops. In order to minimize the damage, it is required to detect the damaged areas and enact the proper procedures in order to save the damaged fields immediately after the disaster. In this paper, we propose indices that can indicate the degree of salt-breezed damages in the early withering-up stage. To detect the indices, high-spatial-resolution hyperspectral data taken in actual damaged paddy fields are analyzed. In addition, the sequential change of hyperspectral data in rice within artificial withering-up experiments is recorded to interpret the fundamental mechanism of the indices. The applicability of the indices for satellite data is also shown by applying them to simulated satellite data. Keywords-component; spectroscopy; rice paddy; hyperspectral; salt-damaged; SPOT5; NDVI; NDGI; withering up;

Development of a Low-Cost, Lightweight Hyperspectral Imaging System Based on a Polygon Mirror and Compact Spectrometers

Low-altitude hyperspectral observation systems using aerial observation with unmanned aerial vehicles (UAVs) have advantages over satellite systems with respect to frequency, accuracy, and spatial resolution. Although low-cost lightweight UAVs have become available in recent years, the current price ranges of lightweight pushbroom and snapshot hyperspectral sensors remain high. For sustainable operation of UAV-mounted hyperspectral sensing, the challenge in production has been shifted from the size and weight to the cost of the lightweight hyperspectral sensors. In this paper, we develop a low-cost, lightweight whiskbroom hyperspectral imaging system. The gross weight of the sensor is 1200 g. The spectral range of the 256-band spectrometer extends from 340 to 750 nm with a 14-nm spectral resolution. The viewing angle across the flight direction is controlled by the rotation of an eight-sided polygon mirror. When the exposure time, flight altitude, flight speed, and focal length of the optical lens are 3.2 ms, 10 m, 10 m/s, and 8 mm, respectively, then the estimated values of the swath and the area coverage per second are 13.4 m and 134.1

Estimation of Lambert parameter based on leaf-scale hyperspectral images using dichromatic model-based PCA

\hbox{m}^2

Leaf Parameter Estimation Based on Leaf Scale Hyperspectral Imagery

/s, respectively. The spatial resolution is 0.97 (m) (flight direction)