Sample collection for the random forest algorithm input (republished satellite data under a CC BY license, with permission from the Department of Remote Sensing, Ministry of Natural Resources and Environment of Vietnam, original copyright [1995, 2004, 2015]).</p
This is the training data for land use/cover classification and it was collected by doing visual int...
Random forest classification results for the whole dataset with stratified k-fold and oversampling.<...
<p>These include only samples filtered by the Random Forest outlier removal step (Biomes follow Olso...
Satellite image classification results (republished satellite data under a CC BY license, with permi...
The visual comparison between the results of the image classification and the Forest inventory maps ...
This work was carried out with the support of “Cooperative Research Program for Agriculture Sc...
The expansion of plantation poses new challenges for mapping forest, especially in mountainous regio...
Input variables used by the Random Forests for the estimation of the direct economic impacts and the...
The accuracy of remotely sensed forest stand maps is traditionally assessed by comparing a sample of...
Forest biomass is an important ecological indicator for the sustainable management of forests. The a...
Five sets of data were separately used as inputs in the object-based random forest classification.</...
<p>These files are supplementary information to illustrate the metadata reports and default visualiz...
ABSTRACT:The maximum likelihood classifier is the most common classifier used in the remote sensing ...
Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a ...
<p>These files represent the source code and technical fitting details of the Random Forest-based po...
This is the training data for land use/cover classification and it was collected by doing visual int...
Random forest classification results for the whole dataset with stratified k-fold and oversampling.<...
<p>These include only samples filtered by the Random Forest outlier removal step (Biomes follow Olso...
Satellite image classification results (republished satellite data under a CC BY license, with permi...
The visual comparison between the results of the image classification and the Forest inventory maps ...
This work was carried out with the support of “Cooperative Research Program for Agriculture Sc...
The expansion of plantation poses new challenges for mapping forest, especially in mountainous regio...
Input variables used by the Random Forests for the estimation of the direct economic impacts and the...
The accuracy of remotely sensed forest stand maps is traditionally assessed by comparing a sample of...
Forest biomass is an important ecological indicator for the sustainable management of forests. The a...
Five sets of data were separately used as inputs in the object-based random forest classification.</...
<p>These files are supplementary information to illustrate the metadata reports and default visualiz...
ABSTRACT:The maximum likelihood classifier is the most common classifier used in the remote sensing ...
Random Forest (RF) is a widely used algorithm for classification of remotely sensed data. Through a ...
<p>These files represent the source code and technical fitting details of the Random Forest-based po...
This is the training data for land use/cover classification and it was collected by doing visual int...
Random forest classification results for the whole dataset with stratified k-fold and oversampling.<...
<p>These include only samples filtered by the Random Forest outlier removal step (Biomes follow Olso...