How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

NDWI--a normalized difference water index for remote sensing of vegetation liquid water from space.

My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiarize our readers with this interesting new racehorse in the Machine Learning stable. Bernhard Scholkopf, in an introductory overview, points out that a particular advantage of SVMs over other learning algorithms is that it can be analyzed theoretically using concepts from computational learning theory, and at the same time can achieve good performance when applied to real problems. Examples of these real-world applications are provided by Sue Dumais, who describes the aforementioned text-categorization problem, yielding the best results to date on the Reuters collection, and Edgar Osuna, who presents strong results on application to face detection. Our fourth author, John Platt, gives us a practical guide and a new technique for implementing the algorithm efficiently.

Support vector machines

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

/pdf/change-detection-techniques-4g34rgajio.pdf

Change detection techniques

Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

/pdf/a-survey-of-image-classification-methods-and-techniques-for-5bc5ltr9dq.pdf

A survey of image classification methods and techniques for improving classification performance

Detection of changes in leaf water content using Near- and Middle-Infrared reflectances

Many sugar maple stands in the northeastern United States experienced extensive insect damage during the 1988 growing season. Chlorophyll data and high spectral resolution spectrometer laboratory reflectance data were acquired for multiple collections of single detached sugar maple leaves variously affected by the insect over the 1988 growing season. Reflectance data indicated consistent and diagnostic differences in the red edge portion (680-750 nm) of the spectrum among the various samples and populations of leaves. These included differences in the red edge inflection point (REIP), a ratio of reflectance at 740-720 nm (RE3/RE2), and a ratio of first derivative values at 715-705 nm (D715/D705). All three red edge parameters were highly correlated with variation in total chlorophyll content. Other spectral measures, including the Normalized Difference Vegetation Index (NDVI) and the Simple Vegetation Index Ratio (VI), also varied among populations and over the growing season, but did not correlate well with total chlorophyll content. Leaf stacking studies on light and dark backgrounds indicated REIP, RE3/RE2 and D715/D705 to be much less influenced by differences in green leaf biomass and background condition than either NDVI or VI.

Red edge spectral measurements from sugar maple leaves

Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline

High-spectral resolution field and laboratory optical reflectance measurements of red spruce and eastern hemlock needles and branches

Current broadband sensors are not capable of separating the initial stages of forest damage. The current investigation evaluates the potential of hyperspectral data for detecting the initial stages of forest damage at the canopy level in the Norway spruce (Picea abies (L.) Karst) forests of Czech Republic. Hyperspectral canopy reflectance imagery and foliar samples were acquired contemporaneously for 23 study sites in August 1998. The sites were selected along an air pollution gradient to represent the full range of damage conditions in even-aged spruce forests. The changes in canopy and foliar reflectance, chemistry and pigments associated with forest damage were established. The potential of a large number of spectral indices to identify initial forest damage was determined. Canopy hyperspectral data were able to separate healthy from initially damaged canopies, and therefore provided an improved capability for assessment of forest physiology as compared to broadband systems. The 673-724 nm region exhibited maximum sensitivity to initial damage. The nine spectral indices having the highest potential as indicators of the initial damage included: three simple band ratios, two derivative indices, two modelled red-edge parameters and two normalized bands. The sensitivity of these indices to damage was explained primarily by their relationship to foliar structural chemical compounds, which differed significantly by damage class.

https://jacquelinepeterson.tamu.edu/access/hist_czech_papers/ASASpaper_IJRS2004_PKECampbell_ForestDamageDetection.pdf

Barrett N. Rock

Papers

Detection of changes in leaf water content using Near- and Middle-Infrared reflectances

Red edge spectral measurements from sugar maple leaves

Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline

High-spectral resolution field and laboratory optical reflectance measurements of red spruce and eastern hemlock needles and branches

Detection of initial damage in Norway spruce canopies using hyperspectral airborne data