کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
555946 1451269 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
پیش نمایش صفحه اول مقاله
Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions
چکیده انگلیسی

In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters. We define an active set feature learner that includes in the model only features that improve the classifier. To this end, we consider a fast and linear classifier, multiclass logistic classification, and show that with a good representation (the filters discovered), such a simple classifier can reach at least state of the art performances. We apply the proposed active set learner in four hyperspectral image classification problems, including agricultural and urban classification at different resolutions, as well as multimodal data. We also propose a hierarchical setting, which allows to generate more complex banks of features that can better describe the nonlinearities present in the data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 105, July 2015, Pages 272–285
نویسندگان
, , ,