A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges

By Sicong Liu, Daniele Marinelli, Lorenzo Bruzzone, Francesca Bovolo. IEEE Geoscience and Remote Sensing Magazine, 2019

351 citations recorded by the source corpus.

The expected increasing availability of remote sensing satellite hyperspectral (HS) images provides an important and unique data source for Earth observation (EO). HS images are characterized by a detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range, which makes it possible to monitor land-cover dynamics at a fine spectral scale. This is due to its capability of detecting subtle spectral variations in multitemporal images associated with land-cover changes that are not detectable in traditional multispectral (MS) images because of their limited spectral resolution (i.e., sufficient for representing only abrupt, strong changes in the spectral signature, as a rule). To fully exploit the available multitemporal HS images and their rich information content in change detection (CD), it is necessary to develop advanced automatic techniques that can address the complexity of the extraction of change information in an HS space. This article provides a comprehensive overview of the CD problem in HS images, as well as a survey on the main CD techniques available for multitemporal HS images. We review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/15c226a53dfcf1ec3be9fec2363890434ef079d8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Ayesha Shafique
2. Guo Cao
3. Muhammad Aslam
4. Zia Khan
5. Muhammad Asad

In Atmospheric Science

1. Ayesha Shafique
2. Guo Cao
3. Muhammad Aslam
4. Zia Khan
5. Muhammad Asad

In Media Technology

1. Ayesha Shafique
2. Guo Cao
3. Muhammad Aslam
4. Zia Khan
5. Muhammad Asad

Citations in the corpus

• Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, background): “Existing change detection reviews have focused mainly on the design of change detection techniques in multi-temporal hyperspectral images (HSIs) and high-spatial-resolution images [1-4].”
• Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “HSI can provide more spectral information than SAR [33] and MS images [79].”