A shape-based approach to change detection of lakes using time series remote sensing images

Abstract

Abstract-Shape analysis has not been considered in remote sensing as extensively as in other pattern recognition applications. However, shapes such as those of geometric patterns in agriculture and irregular boundaries of lakes can be extracted from the remotely sensed imagery even at relatively coarse spatial resolutions. This paper presents a procedure for efficiently retrieving and representing shapes of interesting features in remotely sensed imagery using supervised classification, object recognition, parametric contour tracing, and proposed piecewise linear polygonal approximation techniques. In addition, shape similarity can be measured by means of a computationally efficient metric. The study was conducted on a time series of radiometric and geometric rectified Landsat Multispectral Scanner (MSS) images and Thematic Mapper (TM) images, covering the scenes containing lakes in the Nebraska Sand Hills region. The results validate the effectiveness of the proposed processing chain in change detection of lake shapes and show that shape similarity is an important parameter in quantitatively measuring the spatial variations of objects.

Change Detection in Remote Sensing Images Using Conditional Adversarial Networks

“…The Direct Object change detection (DOCD) approach is based on the comparison of object geometrical properties (Lefebvre et al, 2008;Zhou et al, 2008), spectral information (Miller et al, 2005;Hall and Hay, 2003) or texture features (Lefebvre et al, 2008;Tomowski et al, 2011). In Classified Objects change detection (COCD) approach the extracted objects are compared based on the geometry and class labels (Chant, Kelly, 2009;Jiang and Narayanan, 2003). The framework based on post-classification (Blaschke, 2005) presumes extracting objects and independently classifying them (Im and Jensen, 2005;Hansen and Loveland, 2012).…”

Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset

“…The data published per day for this spatial program corresponds to more than 15 TB of images from multiple sensors (Sentinel-1, Sentinel-2, Sentinel-3, and Sentinel-5). Several methods were developed to use satellite image time series (SITS) for land cover mapping [1], change detection [8], tree species detection [9], or crop classification [10]. Thanks to the high revisiting period, Sentinel imagery (whether SAR for Sentinel-1 or optical for Sentinel-2), many works showed the importance of these images for Land Use/Land Cover (LULC) mapping both for optical [11], and SAR imagery [12].…”

Hybrid multi-GPU computing: accelerated kernels for segmentation and object detection with medical image processing applications

“…Blob/tubular detection refers to methods that are aimed at detecting clustered points in the image that are either brighter or darker than the surrounding region. The shape-based object detection in images is an important step in the analysis of large-scale scientific data, as for example: satellite and Unmanned Aerial Vehicles (UAVs) images for human, fire and building detection, lakes detection from radiometric and geometric rectified Landsat Multispectral Scanner (MSS) images and Thematic Mapper (TM) images, light points detection, blob detection in infrared images, medical imaging and others [9,17,29,37]. In the medical imaging context, the detection of bleeding/blood regions in WCE images, bright lesions in fundus images, nodule detection in thorax X-ray images, nuclei detection in microscopic zebrafish images, enhancement of vascular structures, detection of lesions in images of multiple sclerosis patients, tumor detection based on blob detection, [10,12,14,16,22,27,32,39] represent some of the examples addressed by the community.…”