Abstract
The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.
Highlights
An important advance in the field of earth observation is the discovery of spectral indices, they have proved their effectiveness in surface description
The results shows that none of optimized models outperforms the previous performance with the initial image processing
The results allow us to conclude that any simple linear combination is just more efficient (+4.87% mean intersection over union (mIoU)) than any standard indices by taking into account all spectral bands and few transformations
Summary
An important advance in the field of earth observation is the discovery of spectral indices, they have proved their effectiveness in surface description. Several studies have been conducted using remote sensing indices, often applied to a specific field of study like evaluations of vegetation cover, vigor, or growth dynamics [1,2,3,4] for precision agriculture using multi-spectral sensors. Remote sensing indices can be used for other surfaces analysis like water, road, snow [8] cloud [9] or shadow [10]. There are two main problems with these indices They are almost all empirically defined, the selection of wavelengths comes from observation, like NDVI for vegetation indices. An optimization of NDVI ( N IR − Red)/( N IR + Red) was proposed by [12] under the name of WDRVI (Wide Dynamic Range Vegetation Index)
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