Multitask remote sensing data classification

Many remote sensing data processing problems are inherently constituted by3several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task. Here, the relation to previous acquisitions should be properly considered because of the nonstationary behavior of temporal, spatial, and angular image features which gives rise to distribution changes. This phenomenon is known as covariate shift. Additionally, when labeled data are scarce or expensive to obtain, the small sample-set problem arises, which makes solving the problems independently in each domain difficult. Multitask learning (MTL) aims at jointly solving a set of prediction problems by sharing information across tasks. This paper introduces MTL in remote sensing data classification

cloud screening; covariate shift (cs); data set bias; domain adaptation; image classification; land-mine detection; multitask learning (mtl); support vector machine (svm); urban monitoring

Multitask remote sensing data classification Articles