Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system

Figures & data

Figure 1. Study area: left corner highlight the area seriously impacted by invasive vegetation Cogon Grass.

Table 1. Land cover classes in the study area.

Class ID	Class name	Description
CG	Cogon Grass	Cogon Grass (Imperata cylindrica) is an invasive, nonnative grass which occurs in Florida and several other Southeastern US states.
IP	Improved Pasture	A sown pasture that includes introduced pasture species, usually grasses in combination with legumes. These are generally more productive than the local native pastures, have higher protein and metabolizable energy and are typically more digestible. In our case, we also assume it is not infested by Cogon Grass.
SUs	Saw Palmetto Shrubland	Saw Palmetto (Serenoa repens) dominant shrubland.
MFB	Broadleaf Emergent Marsh	Broadleaf emergent dominated freshwater marsh. It can be found throughout Florida.
MFG	Graminoid Freshwater Marsh	Graminoid dominated freshwater marsh. It can be found throughout Florida.
FHp	Hardwood Hammock – Pine Forest	A co-dominate mix (40/60 to 60/40) of Slash Pine (Pinus elliottii var. densa) with Laural Oak (Quercus laurifolia), Live Oak (Q. virginiana), and/or Cabbage Palm (Sabal palmetto).
Shadow	Shadow	Shadow of all kinds of objects in the study area.

Table 2. Summary of sensor and flight procedure.

Items	Description
UAS type	Light UAS with Fixed wing
Sensor name	Canon EOS REBEL SL1
Sensor type	CCD
Pixel dimension	5184 x 3456
Length of focus	20mm
Sensor size	22.3 x 14.9 mm
Channels	RGB
Takeoff time	29/10/2015 16:54:51 EDT^a
Landing time	29/10/2015 17:49:33 EDT^a
Takeoff latitude	27.22736549
Takeoff longitude	−81.51152802
Average wind speed	5.1 m/s
Average altitude	302.7 m
Average pixel size	6.5 cm
Forward overlap	83%
Side overlap	50%
across-track
along-track

^aEastern Daylight Time.

^bField of view in degree.

Figure 2. Two options for generating training samples: (a) option I labels the surrounding pixels as background regardless of their actual class types and (b) option II labels all the pixels with their true labels.

Figure 3. Flow chart of conducted experiments.

Figure 4. Flow chart of conducted experiments.

Figure 5. Overall accuracies obtained from five classification methods with three sets of training samples.

Figure 6. Producer and user accuracies for different classification methods with different sets of training samples.

Figure 7. Maps generated by five classification methods using 3500 training samples.

Figure 8. Zoom-in version of maps focusing on Cogon grass area.

Table 3. Confusion matrix for FCN-II-OBIA classification results.

	CG	IP	SUs	MFB	MFG	FHp	Shadow	Producer accuracy
CG	190	9	0	0	1	0	0	95.0
IP	15	164	3	0	11	2	5	82.0
SUs	3	0	151	0	3	18	25	75.5
MFB	1	0	0	176	17	0	6	88.0
MFG	2	6	10	14	149	7	12	74.5
FHp	1	0	10	0	2	176	11	88.0
Shadow	1	10	8	0	2	10	169	84.5
User accuracy	89.2	86.8	83.0	92.6	80.5	82.6	74.1	83.9