English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Sparse representation is widely applied in the field of remote sensing image classification, but sparsity-based methods are time-consuming. Unlike sparse representation, collaborative representation could improve the efficiency, accuracy, and precision of image classification algorithms. Thus, we propose a high-resolution remote sensing image classification method using collaborative representation with a locally adaptive dictionary. The proposed method includes two steps. First, we use a similarity measurement technique to separately pick out the most similar images for each test image from the total training image samples. In this step, a one-step sub-dictionary is constructed for every test image. Second, we extract the most frequent elements from all one-step sub-dictionaries of a given class. In the step, a unique two-step sub-dictionary, that is, a locally adaptive dictionary is acquired for every class. The test image samples are individually represented over the locally adaptive dictionaries of all classes. Extensive experiments (OA (%) =83.33, Kappa (%) =81.35) show that our proposed method yields competitive classification results with greater efficiency than other compared methods.

Land-use Classification for High-resolution Remote Sensing Image using Collaborative Representation with a Locally Adaptive Dictionary