A regional land use survey based on remote sensing and other data: A report on a LANDSAT and computer mapping project, volume 2

The author has identified the following significant results. The project mapped land use/cover classifications from LANDSAT computer compatible tape data and combined those results with other multisource data via computer mapping/compositing techniques to analyze various land use planning/natural resource management problems. Data were analyzed on 1:24,000 scale maps at 1.1 acre resolution. LANDSAT analysis software and linkages with other computer mapping software were developed. Significant results were also achieved in training, communication, and identification of needs for developing the LANDSAT/computer mapping technologies into operational tools for use by decision makers

CirdoX: an On/Off-line Multisource Speech and Sound Analysis Software

International audienceVocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. CIRDOX, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which CIRDOX was involved are described. They were operated in real environment, namely a Living lab environment. Keywords: audio and speech processing, natural language and multimodal interactions, Ambient Assisted Living (AAL)

Hierarchical Classification in High Dimensional, Numerous Class Cases

As progress in new sensor technology continues, increasingly high resolution imaging sensors are being developed. HIRIS, the High Resolution Imaging Spectrometer, for example, will gather data simultaneously in 102 spectral bands in the 0.4 - 2.5 micrometer wavelength region at 30 m spatial resolution. AVIRIS, the Airborne Visible and Infrared Imaging Spectrometer, covers the 0.4 - 2.5 micrometer in 224 spectral bands. These sensors give more detailed and complex data for each picture element and greatly increase the dimensionality of data over past systems. In applying pattern recognition methods to remote sensing problems, an inherent limitation is that there is almost always only a small number of training samples with which to design the classifier. Both the growth in the dimensionality and the number of classes is likely to aggravate the already significant limitation of training samples. Thus ways must be found for future data analysis which can perform effectively in the face of large numbers of classes without unduly aggravating the limitations on training. A set of requirements for a valid list of classes for remote sensing data is that the classes must each be of informational value (i.e. useful in a pragmatic sense) and the classes be spectrally or otherwise separable (i.e., distinguishable based on the available data). Therefore, a means to simultaneously reconcile a property of the data (being separable) and a property of the application (informational value) is important in developing the new approach to classifier design. In this work we propose decision tree classifiers which have the potential to be more efficient and accurate in this situation of high dimensionality and large numbers of classes; In particular, we discuss three methods for designing a decision tree classifier, a top down approach, a bottom up approach, and a hybrid approach. Also, remote sensing systems which perform pattern recognition tasks on high dimensional data with small training sets require efficient methods for feature extraction and prediction of the optimal number of features to achieve minimum classification error. Three feature extraction techniques are implemented. Canonical and extended canonical techniques are mainly dependent upon the mean difference between two classes. An autocorrelation technique is dependent upon the correlation differences, The mathematical relationship between sample size, dimensionality, and risk value is derived. It is shown that the incremental error is simultaneously affected by two factors, dimensionality and separability. For predicting the optimal number of features, it is concluded that in a transformed coordinate space it is best to use the best one feature when only small numbers of samples are available. Empirical results indicate that a reasonable sample size is six to ten times the dimensionality

RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA

Recently, Synthetic Aperture Radar (SAR) satellite imaging has become an increasing popular data source especially for land cover mapping because its sensor can penetrate clouds, haze, and smoke which a serious problem for optical satellite sensor observations in the tropical areas. The objective of this study was to determine an alternative method for land cover classification of ALOS-PALSAR data using Random Forest (RF) classifier. RF is a combination (ensemble) of tree predictors that each tree predictor depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. In this paper, the performance of the RF classifier for land cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual polarization) in the area of Jambi and South Sumatra, Indonesia. Overall accuracy of this method was 88.93%, with producer’s accuracies for forest, rubber, mangrove & shrubs with trees, cropland, and water classes were greater than 92%

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data