The advent of new satellite missions offering high spatial, spectral, and temporal resolution has significantly enhanced the possibility to monitor vegetation and agricultural practices. The High-resolution (HR) Satellite Image Time Series (SITS) enables a deeper understanding of crop fields behavior and precise boundary detection. While Convolutional Neural Networks (CNNs) have demonstrated effectiveness in crop fields-related analyses, existing methods for crop boundary detection often focus on mono-temporal image analysis, overlooking valuable multi-temporal information in SITS. To address this gap, we propose the utilization of a UNet-based three-dimensional (3D) CNN architecture, allowing for the simultaneous modeling of spatial-temporal information within multi-spectral multi-temporal SITS. Additionally, we explore various CNN-based U-Net models to further validate the proposed approach in accurately detecting crop field boundaries. The method is evaluated in an agricultural area in Germany using 12 Sentinel-2 Level-2A images and has demonstrated promising results.

In recent years, deep learning methods, in particular Convolutional Neural Networks (CNNs), have been increasingly used in Change Detection (CD). However, most CNN-based CD methods are primarily designed for analyzing only a single pair of images due to the challenge of collecting and constructing ground reference data during the system-training phase. Consequently, existing CD methods, particularly those focused on detecting multi-annual changes, exhibit limited capability in extracting comprehensive spatio-temporal information. To address this limitation, we propose a novel weakly supervised deep learning-based technique for CD exploiting a 3D CNN architecture to extract spatio-temporal information. Our technique incorporates a fine-tuning stage to effectively capture temporal patterns from a yearly Satellite Image Time Series (SITS) by using different 3D convolutional layers. It also exploits a multi-feature hyper-temporal Change Vector Analysis (CVA) for multi-annual change identification. The proposed approach is tested on a four year dataset in Amazonia and gained the highest yearly CD accuracy of 88.59%, 97.27% and 87.87% for 2017, 2018 and 2019, respectively.

High-resolution (HR) satellite image time series (SITS) are a valuable data source for analyzing land cover change (LCC) due to their large amount of spatial, spectral, and temporal information. However, most existing LCC detection methods focus on binary change detection (CD) within a single year and fail to provide detailed information about the specific type of change. In this study, we propose a multiannual CD approach that identifies changes occurring between consecutive years and provides information about the type of LC transition. The proposed approach exploits multiannual and multispectral SITS to generate a hypertemporal feature space (FS). This FS is analyzed to create a set of CD maps that indicate the time, probability, and type of change. To measure the similarity between pixel time series, we use dynamic time warping (DTW) in the space of hypertemporal features. A hierarchical clustering technique is exploited to develop a set of class prototypes (CPs) that represent the characteristics of different LC classes. The CPs are then used to identify the most probable LC transition for each changed pixel. Two test areas were selected to evaluate the effectiveness of the proposed approach. The first one is located in Amazon and spans the years 2015 to 2019; and the second one is located in Sahel-Africa and covers the years 2015 and 2016, using multiannual Landsat 7 and 8 SITS. The results demonstrate that the proposed approach is effective in detecting multiannual changes and in identifying the LC transitions.

A great effort has been put on developing technologies that can process High Resolution (HR) satellite datasets to properly monitor the environmental changes and produce long term Change Detection (CD) maps. However, there is still a need to design CD approaches that process Satellite Image Time Series (SITS) with high spatial, spectral, and temporal resolution and describe changes that have occurred between the consecutive years. Here, a CD processing chain is proposed that: i) extracts several relevant features of the spectral trends of different sets of LC changes, ii) produces a regular and dense feature time series, iii) analyzes differences between the consecutive years by using a Multi-feature Hyper-temporal Change Vector Analysis (MHCVA) technique, and iv) detects the year and the probability of changes at pixel level. The effectiveness of the proposed approach is tested on a multi-annual Landsat 7 and 8 images of an area located in Amazon.

Recent satellite missions have initiated a new era in the area of Satellite Image Time Series (SITS) analysis by providing a huge number of High Resolution (HR) spectral-temporal images. The availability of HR images opens a door to an unprecedented wide range of possibilities to produce and develop high resolution Land Cover (LC) and Land Cover Change (LCC) maps. The goal of this paper is to effectively use high spatio-temporal resolution images to generate LCC maps by defining a novel automatic and unsupervised deep learning method based on three-dimensional (3D) Convolutional Neural Network (CNN). The method extracts spatio-temporal information from long SITS by using a pre-trained 3D CNN, detects changes and locates them in space and time. Experiments have provided promising results over both Amazonia and Saudi Arabia in the period 2013–2017, and has been compared to the other well-known LCC detection method.