Glaciers in the Himalaya are often heavily covered with supraglacial debris,making them difficult to study with remotely-sensed imagery alone.Various methods such as band ratios can be used effectively to map clean-ic...Glaciers in the Himalaya are often heavily covered with supraglacial debris,making them difficult to study with remotely-sensed imagery alone.Various methods such as band ratios can be used effectively to map clean-ice glaciers;however,a thicker layer of debris often makes it impossible to distinguish between supraglacial debris and the surrounding terrain.Previously,a morphometric mapping approach employing an ASTER-derived digital elevation model has been used to map glaciers in the Khumbu Himal and the Tien Shan.This study on glaciers in the Greater Himalaya Range in Zanskar,southern Ladakh,aims (i) to use the morphometric approach to map large debris-covered glaciers;and (ii) to use Landsat and ASTER data and GPS and field measurements to document glacier change over the past four decades.Field work was carried out in the summers of 2008.For clean ice,band ratios from the ASTER dataset were used to distinguish glacial features.For debris-covered glaciers,topographic features such as slope were combined with thermal imagery and supervised classifiers to map glacial margins.The method is promising for large glaciers,although problems occurred in the distal and lateral parts and in the fore field of the glaciers.A multi-temporal analysis of glaciers in Zanskar showed that in general they have receded since at least the mid-to late-1970s.However,some few glaciers that advanced or oscillated - probably because of specific local environmental conditions - do exist.展开更多
Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which ...Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.展开更多
基金the generosity of The University of Montana and the German Research Foundation (DFGBU 949/15-1)a research fellowship from the Alexander von Humboldt Foundation awarded to Ulrich Kamp
文摘Glaciers in the Himalaya are often heavily covered with supraglacial debris,making them difficult to study with remotely-sensed imagery alone.Various methods such as band ratios can be used effectively to map clean-ice glaciers;however,a thicker layer of debris often makes it impossible to distinguish between supraglacial debris and the surrounding terrain.Previously,a morphometric mapping approach employing an ASTER-derived digital elevation model has been used to map glaciers in the Khumbu Himal and the Tien Shan.This study on glaciers in the Greater Himalaya Range in Zanskar,southern Ladakh,aims (i) to use the morphometric approach to map large debris-covered glaciers;and (ii) to use Landsat and ASTER data and GPS and field measurements to document glacier change over the past four decades.Field work was carried out in the summers of 2008.For clean ice,band ratios from the ASTER dataset were used to distinguish glacial features.For debris-covered glaciers,topographic features such as slope were combined with thermal imagery and supervised classifiers to map glacial margins.The method is promising for large glaciers,although problems occurred in the distal and lateral parts and in the fore field of the glaciers.A multi-temporal analysis of glaciers in Zanskar showed that in general they have receded since at least the mid-to late-1970s.However,some few glaciers that advanced or oscillated - probably because of specific local environmental conditions - do exist.
文摘Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.
