Recent advances in deep learning have dramatically improved the performance of content-
based remote sensing image retrieval (CBRSIR) with the same distribution of training set …

In remote sensing field, the image retrieval is considered a complex task and attained higher
attention, because of the data acquired from the earth observation satellites. An …

For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully
express the complexity of the image information. Due to the large volume of remote sensing …

The classification of GM and non-GM maize kernels is fundamental for further analysis of the
gene action in maize. Therefore, a complete and novel detection scheme based on near …

Content-Based Remote Sensing Image Retrieval (CBRSIR) is used to find relevant images
from large collections of remote sensing images. CBRSIR works by indexing each image in …

This research aims to conduct a systematic review of feature extraction techniques in
patterned images. A total of 47 journals were analyzed to identify the techniques used, their …

In the automated modeling generated by oblique photography, various terrains cannot be
physically distinguished individually within the triangulated irregular network (TIN). To utilize …

Recent advancements in satellite technologies have resulted in the emergence of Remote
Sensing (RS) images. Hence, the primary imperative research domain is designing a …

The textural and structural information contained in the images is very important for
generating highly discriminative features for the task of image retrieval. Morphological …

The increasing volume and heterogeneous nature of remote sensing data necessitate
efficient and accurate image retrieval systems, particularly for land use and land cover …