Novel Adaptive Region Spectral–Spatial Features for Land Cover Classification With High Spatial Resolution Remotely Sensed Imagery | IEEE Journals & Magazine | IEEE Xplore

Novel Adaptive Region Spectral–Spatial Features for Land Cover Classification With High Spatial Resolution Remotely Sensed Imagery


Abstract:

Spectral–spatial features are important for ground target identification and classification with high spatial resolution remotely sensed (HSRRS) Imagery. In this article,...Show More

Abstract:

Spectral–spatial features are important for ground target identification and classification with high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features, named the Gaussian-weighting spectral (GWS) feature and the area shape index (ASI) feature, are proposed to complement the deficiency of the basic image feature for land cover classification with HSRRS imagery. The proposed GWS feature is an adaptive region-based feature that aims to improve the spectral homogeneity of a local area surrounding a pixel. Additionally, it is well known that the spectral feature is inadequate for classifying HSRRS imagery. Therefore, one spatial feature called the ASI feature is proposed here to describe the relationship between the area and shape for an adaptive region around each pixel. The proposed GWS and ASI features coupled with the basic red–green–blue (RGB) feature are fed into a supervised classifier to obtain the final classification map. Experiments based on four real HSRRS images demonstrate that the proposed GWS and ASI features are capable of improving classification accuracies compared with some cognate state-of-the-art methods. Moreover, the experiments also reveal that the proposed spectral–spatial features can complement each other for enhancing the classification performance with HSRRS images.
Article Sequence Number: 5609412
Date of Publication: 12 May 2023

ISSN Information:

Funding Agency:


I. Introduction

Remote sensed images have been widely used for observing the earth’s surface, such as change detection [1], [2], [3], [4] and land cover classification [5], [6], [7], [8], [9]. Land cover classification aims to assign a semantic label to each pixel in an image scene. Owing to the quick development of remotely sensed platforms (such as QuickBird satellites, aerial planes, and drones), high spatial resolution remotely sensed (HSRRS) images can be easily obtained [10]. An HSRRS image can capture the ground details, such as shape, boundary, and direction for ground targets, which are very helpful for land cover classification [11], [12], [13], [14], [15]. From the perspective of applications, land cover classification with HSRRS images plays an important role in urban planning [16], target recognition [17], [18], and natural disaster assessment [19], [20].

References

References is not available for this document.