Spectral–Spatial-Aware Unsupervised Change Detection With Stochastic Distances and Support Vector Machines

Abstract

Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods.

CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection

“…Ran et al [46] proposed a spectralspatial one-class sparse representation classifier (OCSRC) method by applying spectral-spatial features to the one class of sparse representation processes instead of the original spectral bands. To solve the challenge of CD caused by artificial objects such as clouds and shadows, Negri et al [47] proposed a novel spectral-spatial-aware unsupervised change detection framework. Zhan et al [48] proposed a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for HSI CD, which extracts the spectral-spatial vector from dual-temporal images, and then uses a Siamese network based on contrast loss to train and optimize the network.…”

Wavelet Spatio-Temporal Change Detection on Multitemporal SAR Images

“…Two choices of cut-off value τ are employed. Otsu (OT) [69] and Kittler-Illingworth (KI) [70] thresholding techniques have been successfully employed for change detection purposes [18], [26], [41], [54]. Both OT and KI thresholding are applied to WECS, ECS and TAAD, whilst CVA and ISFA are thresholded via OT.…”