Ayer Hitam Forest (AHFR) from space using satellite remote sensing

There is a high demand to map and monitor the land use and assess their condition for ecological and economic reasons. Information on existing land and cover and their spatial distribution is a pre-requisite for any planning, development and management programme. In this study, Landsat TM data of 1998 were acquired over the AHFR and it's vicinity which covers an area more than 1, 300 ha. The objective of this paper is to map AHFR and assess the land cover of AHFR in 1998 as well as its surrounding area using remote sensing technology. Digital data processing and analysis were carried out using PCI/EASI PACE software, version 6.2 available in Faculty of Forestry, UPM. A false Colour Composite (FCC) of Landsat TM band 4-5-3 (R-G-B) was used in supervised classification using Maximum Likelihood Classifier (MLC). From a visual interpretation, several features of AHFR could be identified such as federal road, forest road, cleared land, built-up area, oil palm, water bodies and rubber plantation etc. Meanwhile, digital classification showed that seven land use types surrounding AHFR such as forest, secondary forest/shrubs, oil palm, rubber, built-up area, cleared land and water bodies could a easily be mapped out. The mean overall classification accuracy obtained is 86.08 percent with an average accuracy o] 85.64 percent. Satellite map of AHFR is found to be useful for the macro planning and management purposes especially on the Environmental Impact Assessment (EIA) if further development on the area is to be politicized

Fine-scale mapping of vector habitats using very high resolution satellite imagery : a liver fluke case-study

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m(2) and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations

A multi-modal event detection system for river and coastal marine monitoring applications

Abstract—This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network

CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing

We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper