Recent trends and long-standing problems in archaeological remote sensing

The variety and sophistication of data sources, sensors, and platforms employed in archaeological remote sensing have increased significantly over the past decade. Projects incorporating data from UAV surveys, regional and research-driven lidar surveys, the uptake of hyperspectral imaging, the launch of high-temporal revisit satellites, the advent of multi-sensor rigs for geophysical survey, and increased use of structure from motion mean that more archaeologists are engaging with remote sensing than ever. These technological advances continue to drive research in the specialist community and provide reasons for optimism about future applications, but many social and technical obstacles to the integration of remote sensing into archaeological research and heritage management remain. This article addresses the challenges of contemporary archaeological remote sensing by briefly reviewing trends and then focusing on providing a critical overview of the main structural problems. The discussion here concentrates on topics that have dominated the discourse in recent archaeological literature and featured prominently in ongoing fieldwork for the past decade across three broad segments of landscape archaeology: data collection in the field, the current state of data access and archives, and processing and interpretation

Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines